Governance quality and the sustainable development goals: an assessment in Europe Open Access

The Sustainable Development Goals (SDGs), an ambitious global agenda established by the United Nations in 2015, are experiencing delays in their progress (Sachs et al., 2023). Scholars have emphasized the “interconnectedness and indivisibility” of these goals and have called for further research into the critical role of good governance quality in achieving the 2030 Agenda (Bowen et al., 2017).

Public administrations play a crucial role in SDG achievement due to the global nature of these objectives and governments’ unique capacity to establish policies and laws supporting environmental and societal development (Bornemann and Christen, 2024). Additionally, they enhance governance mechanisms and promote collaboration among diverse stakeholders (Abhayawansa et al., 2021; Bisogno et al., 2023). Previous studies point to good governance quality as indispensable for achieving the SDGs (Knox and Orazgaliyev, 2024; Massey, 2022; Omri and Mabrouk, 2020). However, there is a dearth of research on the relationship between governance quality and SDG achievement at the national level (Knox and Orazgaliyev, 2024; Musah, 2024). A few studies have investigated factors that can facilitate or hinder the implementation process of the Goals at the local level (Bisogno et al., 2023). However, understanding the nexus among the different features of governance quality and the SDGs at the national level could be helpful for governments as well as supranational organizations to streamline their efforts toward sustainable development. Our study aims to contribute to this most recent strand of research (Adebayo et al., 2025) by examining whether and to what extent governance quality, expressed through various factors, influences governments’ capacity to achieve these goals. This allows for an articulate analysis of the different features of governance quality and their effects on SDG implementation.

To achieve the aim of the research, this study has been grounded in the current literature on the SDGs, with particular attention paid to studies exploring the various components of good governance quality (Ahmed and Anifowose, 2024; Vazquez-Brust et al., 2020). Referring to the policy process theory framework (Siddiki, 2022) and using the Worldwide Governance Indicators (WGIs) model developed by Kaufmann et al. (2010) and recently updated (Kaufmann and Kraay, 2023), the study analyzes data from 35 European countries over the period 2015–2022, resulting in a panel dataset comprising 245 observations. The SDG Index, published by the Sustainable Development Solutions Network, measures progress toward achieving the SDGs. This methodology explores the relationship between the SDGs and various dimensions of governance quality by testing different models to address the variables encompassed by the WGIs, as the following section will clarify.

The findings confirm a nexus between governance quality and SDG achievement, providing empirical evidence that countries with higher governance quality— specifically, with more transparency, governance stability and effectiveness, better regulatory systems, and corruption control—tend to make more significant progress toward sustainable development. To reinforce our results, we first analyzed overall SDG performance and then disaggregated the SDG index into four subindices according to the SDG “Wedding cake” approach (Bisogno et al., 2024). Our study follows and expands a line of research discussing how enhancing governance quality can facilitate SDG achievement by empirically examining the link between governance quality and the SDGs in the European context.

The results have significant implications for scholars and policymakers. For scholars, the study demonstrates the need to explore the governance-SDG nexus in other continents to develop a comprehensive understanding of how different features of good governance quality contribute to global sustainability. For policymakers, the findings highlight the importance of implementing policies and measures to enhance accountability, governance stability and effectiveness, regulatory systems, and corruption control.

The remainder of the article is structured as follows. Section 2 frames the study in the context of the previous literature, discusses the concept of governance, and provides the research hypothesis. Section 3 explains the methodology, and Section 4 presents and discusses the results of the analysis. Section 5 concludes, in addition to indicating some limitations and future lines of research.

The 2030 Agenda, approved by all members of the United Nations in 2015, promises to transform our world. The Agenda is the result of a complex process that started long ago with the so-called Agenda 21, agreed upon at the Earth Summit in Rio de Janeiro in 1992, and later with the Millennium Declaration (Massey, 2022). The SDGs have attracted the attention of scholars from almost all disciplines (Kaur et al., 2025; Manes-Rossi et al., 2024), with a recent strand of research investigating the link between governance and SDG achievement (Adebayo et al., 2025; Knox and Orazgaliyev, 2024; Massey, 2022; Omri and Mabrouk, 2020). Similarly, the Organization for Economic Co-operation and Development (OECD) suggests several ways to face the challenges related to SDG implementation, highlighting the role of good governance quality as an accelerator (OECD, 2019a).

The concept of governance has been outlined using different approaches (Osborne, 2006), such as the “socio-political governance” approach (Kooiman, 1999), governance as an “intra-organizational network” (Kickert, 1993), or as a proxy for public administration management reforms (Salamon, 2002; Kettl, 2005). The governance perspective provides a useful structure to interpret the changing process of governing (Stoker, 1998). It offers a nuanced framework for understanding how a constellation of features that are not necessarily connected to each other through linear relationships can concur to achieve specific outcomes (Zarghami, 2025).

According to Siddiki (2022), governance can be investigated using the policy process framework, based on the idea that governance comprises the formal and informal processes steering society and the economy in pursuit of common goals (Ansell and Torfing, 2022; Siddiki, 2022), as in the case of the SDGs. This requires institutions with sufficient flexibility to allow experimentation, institutions that foster inclusive deliberation, knowledge-sharing, and joint learning (Carstensen et al., 2023). These processes should be considered interactive, where actors from inside and outside government have a shared interest in influencing policy to identify and pursue common objectives (Deleon and Weible, 2010). The policy process framework focuses on the collective actions of these actors, with the central questions addressing the factors that support and/or challenge collective actions and the outputs/outcomes of these collective actions. According to Siddiki (2022), this approach overlaps with that adopted in governance research, as the main questions are substantially the same: (1) What enables effective governance? (2) What are the outputs/outcomes of governance? (Koontz, 2014; Koontz and Thomas, 2006; Siddiki et al., 2017). The first question can be addressed by considering the different features of governance, the main issue being how to operationalize this complex concept. The second issue can be investigated by examining the achievement of SDGs as the concrete outcome of public policies.

Osborne (2006) argues that although the various approaches proposed in the literature are legitimate within their parameters and research contexts, they cannot capture the “pluralist complexities” (p. 381) of public administration and management (Kaye-Essien, 2025). These approaches have resulted in differing definitions (Klijn and Koppenjan, 2000). Gjaltema et al. (2020) claim that some approaches risk becoming too abstract, leading to ideal types (in the Weberian sense) that cannot be easily translated into concrete measures to assess governance quality. A more concrete and normative framework would provide better support to operationalize the governance concept and clarify its effects through empirical research that considers what is being measured. In doing so, operationalization can permit different elements to coexist (Osborne, 2006), such as rules, fulfilling responsibilities and commitments, and the results of these commitments (Da Cruz and Marques, 2017; UNDP, 2007). Combining different elements and conditions that can coexist in governance facilitates a deeper understanding of how to achieve successful outcomes (Zarghami, 2025). Consequently, several indicators can be used since referring to a single measure or indicator could “easily produce perverse assessment … and will rarely reflect the full situation” (UNDP, 2007, p. 12).

Among the various composite indicators of governance, the Worldwide Governance Indicators (WGIs) developed by Kaufmann et al. (2010) and further updated by Kaufmann and Kraay (2023) have been widely used in the literature. The main advantage of the WGIs model is that it “draw[s] on existing notions and seek[s] to navigate between overly broad and narrow definitions” (Kaufmann et al., 2010, p. 4). Thus, governance is interpreted as the traditions and institutions by which authority in a country is exercised, and three macro dimensions are considered:

1. The process by which governments are selected, monitored, and replaced. This dimension is expressed through the following features:

   • Voice and accountability (VA) measure perceptions of the extent to which citizens participate in selecting their government, freedom of expression and association, and free media.

   • Political stability and the absence of violence/terrorism (PS) represent the likelihood of political instability and violence (including terrorism and ethnic, religious, or regional conflicts).

2. The capacity of a government to effectively formulate and implement policies, expressed by two facets:

   • Government effectiveness (GE) measures the perception of the quality of public services, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies.

   • Regulatory quality (RQ) refers to the perceptions of governments’ ability to formulate and implement policies and regulations that permit and encourage private sector development.

3. The respect of citizens and the state for the institutions that govern economic and social interactions. Two features represent this dimension:

   • Rule of law (RL) captures the perceptions of confidence in and willingness to abide by the rules of society, particularly in terms of the likelihood of crime and violence and the quality of contract enforcement, property rights, the police, and courts.

   • Control of corruption (CC) expresses the perceptions of the extent to which public power is used for private gain.

The data used to assess the quality of governance, along with these dimensions, are standardized through statistical techniques based on a wide range of perception-based sources. Even though the WGIs have been criticized (Langbein and Knack, 2010), the literature has documented several advantages (Lee and Whitford, 2009), which have led to the widespread use of this framework to represent governance (Da Cruz and Marques, 2017). Therefore, it is used in this study to investigate whether and to what extent governance quality features affect SDG development.

The second main issue of the policy process framework is based on identifying the outcomes of governance, which in this study are represented by the SDGs. Previous literature has examined different governance features to explore their relationship with specific SDGs. The basic idea underlying these studies is that effective governance of public-sector entities is necessary to attain the 2030 Agenda’s objectives. As Güney (2017) pointed out, scholars have frequently focused only on specific dimensions of the broad concept of governance quality or have examined only some of the SDGs.

Scholars (Vazquez-Brust et al., 2020) have investigated collaborative governance, documenting the varied roles of different types of collaboration as a governance mechanism to achieve the SDGs. However, the positive outcomes of collaboration may be effective only for certain SDGs; the type of partners involved can also play a role.

Abhayawansa et al. (2021) contend that governments can create value for society and the economy by implementing the SDGs. Searching for a national governance structure that can support this value-creation process, they emphasize the pivotal role of global governance in sustainability and call for further studies on governance at the national level to support SDG achievement.

Government accountability is a fundamental requisite of good governance quality since it shows how policies addressing sustainability issues have been operationalized and implemented. Ríos et al. (2024) propose transparency and accountability as cornerstones of participation for governments, enterprises, civic society, and citizens, serving as a foundation for wise public resource management. Citizens and the media seek to hold public-sector entities accountable, especially regarding highly relevant topics (Trautendorfer et al., 2024), and they are increasing pressure on politicians to promote sustainable policies that protect the environment for future generations (Cuadrado-Ballesteros et al., 2014). A transition process is necessary to integrate the SDGs into governments’ objectives and actions (Benito et al., 2023), and sufficient political support is needed to prioritize sustainability in the political agenda. Politicians should be accountable for their efforts to implement SDG-compliant policies, which improve citizens’ well-being and overall governance quality. The policy framework emphasizes that among the factors enabling collaboration and enhancing governance quality, trust and accountability play significant roles (Bryson et al., 2006), coupled with understanding political dynamics. Accordingly, politicians must implement policies consistent with achieving the SDGs, namely, reducing inequalities, fighting poverty, enhancing quality education, ensuring access to affordable and clean energy, and contributing to decent work and economic growth, among others. Therefore, holding politicians accountable for their actions is one of the primary ways to reduce political opportunism and compel leaders to implement sustainable policies. Reducing opportunistic behavior results in governments performing their functions better, leading to improved governance quality (Cuadrado-Ballesteros and Bisogno, 2021).

A further stream has investigated governmental stability and effectiveness, considering good governance quality a key priority for promoting effective public administrations and attaining the SDGs. Thus, stability serves as an enduring characteristic of public governance and is also essential in the organizational architecture of government systems. When these are stable, they favor solid patterns of behavior over long periods (Trondal, 2023), which can be conducive to implementing the SDGs. Scholars have noticed that reforms implemented under the New Public Management paradigm have mainly concentrated on the efficiency of public-sector entities, while dealing with the SDGs means considering their effectiveness. This is consistent with the idea of focusing on the outputs/outcomes of policy processes (Siddiki, 2022). The OECD (2019b) has proposed several paths to address the complex challenges of implementing the SDGs. Among other issues, good governance quality systems are believed to be central (Knox and Orazgaliyev, 2024). Focusing on developing countries, an adaptive governance framework has been proposed to facilitate SDG implementation (Xue et al., 2018; Musah, 2024).

Knox and Orazgaliyev (2024) have analyzed progress in achieving the SDGs in six Asian countries in relation to the WGIs, concluding that regime type affects sustainable development and that some adaptation should be considered in autocratic regimes, where SDG 16 is difficult to meet. A recent study analyzing governance quality and sustainable development in Sub-Saharan African nations (Adebayo et al., 2025) uses the WGIs, but it measures sustainable development considering other variables than the SDG Index. They confirm the positive relationship between governance quality and sustainable development in its three components: economic, social, and environmental. Both studies analyze a context that is radically different from the European one, and thus, different conclusions might emerge.

It is evident that governments should design and implement governance structures and practices in line with communities’ needs to enhance their effectiveness while balancing social, economic, and environmental growth (Armstrong and Li, 2022).

Another stream of research concentrates on corruption. Although some empirical evidence related to specific contexts supports the “grease the wheels” hypothesis, proposing that corruption may lead to future development by circumventing regulations and bureaucratic bottlenecks (Absalyamova et al., 2016; Achim, 2017; Hope, 2022), the literature supports the “sand the wheels” hypothesis, positing that good corporate governance mitigates agency conflicts and contributes to reducing corruption (Ahmed and Anifowose, 2024). This stream has also emphasized that corruption is a major factor hindering economic growth and sustainable development (Murphy and Albu, 2018), especially in developing countries where the rule of law and degree of corruption significantly impact economic growth (Adebayo et al., 2025; Musah, 2024).

Based on the previous discussion and to contribute to the body of research examining the relationship between the SDGs and governance quality, which is defined by its related elements and components (the dimensions of the WGI index), the following hypothesis is proposed:

This study uses a sample comprising 35 European countries during the period 2015–2022, resulting in a panel dataset with 245 observations. The countries included in the sample are Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxemburg, Malta, the Netherlands, Norway, Poland, Portugal, Romania, Serbia, the Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Ukraine, and the United Kingdom.

The decision to compose the sample in this way was made considering data availability about levels of SDG implementation. As described below, the information has been retrieved from the “SDG Index Report” published annually by the “SDG Transformation Center,”^[1] an organization supported by the “Sustainable Development Solutions Networks” of the United Nations. The first report describing an index of SDG implementation used a sample of 34 countries worldwide. Although it included some Asian and American countries, 24 of the 34 countries were European. The number of countries this organization values has increased over the years, including countries from all over the world and reaching a total of 163 in its most recent report, published in 2023. To use as complete a sample as possible, we decided to focus our study on the European context since these countries have the most data available for the whole period (2015–2022).

The choice of the initial year of the period coincides with the first complete “SDG Index Report,” published in 2015. The last year of the analysis period is 2022 due to data availability for the other central concept in this study: governance quality. As described below, it is represented by the Worldwide Governance Indicators, elaborated by Kaufmann et al. (2010), updated by Kaufmann and Kraay (2023), and published by the World Bank. All these variables are described below.

The hypothesis is empirically checked by the following model:

where α and β are the parameters to be estimated, and sub-indexes i and t refer to each country and year, respectively. The error term has been broken down into two elements: ε_it is the classic disturbance term, and η_i refers to unobservable heterogeneity, i.e. the characteristics of each country that differ among countries but are invariant over time.

The definitions and data sources of the variables are shown in Table A1 in the  Annex. Specifically, the dependent variable is the SDG index published by the “Sustainable Development Solutions Network”^[2]. It takes values between 0 and 100, representing the country’s SDG performance and achievement. This index is based on 110 indicators representing the SDG targets. To make the data comparable, each indicator is rescaled from 0 to 100, from the worst to the best performance. After this normalization, the indicators are grouped according to the 17 SDGs, and the arithmetic mean is calculated. These scores are then averaged across the 17 SDGs to obtain the SDG score. The report applies equal weight to every SDG to reflect policymakers’ commitment to treating all the SDGs equally (Lafortune et al., 2018). To ensure the reliability of the methodology, the SDG Index has been peer-reviewed by Cambridge University Press (Sachs et al., 2022) and Nature Geoscience (Schmidt-Traub et al., 2017) and statistically audited by the European Commission Joint Research Center (Papadimitriou et al., 2019).

Regarding the independent variables, WGIj refers to the j Worldwide Governance Indicators proposed by Kaufmann et al. (2010), as updated by Kaufmann and Kraay (2023): Voice and accountability (WGI_va), Political stability and absence of violence/terrorism (WGI_ps), Government effectiveness (WGI_ge), Regulatory quality (WGI_rq), Rule of law (WGI_rl), and Control of corruption (WGI_cc). Every indicator takes a value ranging from −2.5 (low governance quality) to 2.5 (high governance quality) after a process of aggregation to rescale the individual source data (0–1) into a weighted average of the individual indicators for each source using the unobserved components model. Kaufmann et al. (2010) noted that the six dimensions of governance are not independent, so they should be included in the model one by one. Accordingly, six equations are estimated, one for each WGI indicator, to allow for the effect of each governance feature on SDG implementation.

The model also includes some control variables that can contribute to explaining SDG implementation levels. Following the policy process framework, political dynamics are worthy of investigation to understand how limited public resources can be allocated to respond to perceived public problems (Anyebe, 2018; Siddiki, 2022).

Firstly, the partisan model (Hibbs, 1977) considers governments to be led by ideology since parties represent the interests of different segments of the electorate that can be considered left- or right-wing. Cusack (1997) documents that parties with different ideologies differ in managing public resources and defining the objectives to pursue. Specifically, citizens supporting left-wing parties tend to be more concerned about sustainable development and environmental quality than citizens supporting right-wing parties (Iizuka, 2016; Bisogno et al., 2023).

Secondly, Roubini and Sachs’s hypothesis (RSH) states that large coalition governments usually face coordination problems among the parties involved in the government due to a diversity of political orientations and public priorities (Roubini and Sachs, 1989a, b). Accordingly, inconsistent compromises are more likely to occur in fragmented governments, which could lead to policy implementation failure (Benito et al., 2015), a postponement of policy changes (Ashworth et al., 2005), or a halt in decision-making (Koskimaa and Raunio, 2025).

According to these arguments, it can be argued that work still needs to be done to achieve a broad commitment across parties and depolarize SDG debates. Thus, the model controls two political factors: Left and Strength. Left is a dummy variable that takes the value 1 if the country has a left-wing government and 0 otherwise; Strength refers to the proportion of total seats held by the governing party as a way of representing government fragmentation.

The model additionally includes three other variables, Balance, Population, and Young. The first reflects the central government’s net lending/borrowing as a percentage of the country’s GDP. Financial obligations provided for external debt and a decreasing ability to access international capital can impede SDG advancement (Haughton and Keane, 2021). Marti and Cervelló-Royo (2023) conclude that a solid economic and financial position is linked to optimal SDG achievement in high-income areas. However, Guillamón et al. (2025) evidence that larger deficits are associated with higher SDG scores, suggesting that fiscally challenged governments may be driven to prioritize sustainable initiatives to attract investment and improve their international standing.

The second variable is the total population of the country. Population growth is challenging for sustainable development (UN, 2014). Therefore, it is a key factor to consider when contemplating humans’ future well-being and interactions with the environment and natural resources (Abel et al., 2016). Mutiarani and Siswantoro (2020) show a positive link between municipal population and SDG implementation, but Benito et al. (2023) do not find a significant relationship.

The third variable is the population between the ages of 20 and 34 as a percentage of the total population. This group may have attitudes or perceptions that favor sustainability more than those of older generations (Klimkiewicz and Oltra, 2017). This tendency could be due to their prolonged exposure to discussions about sustainability issues across various contexts (e.g. food, water, pollution, production, consumption). Bisogno et al. (2023, 2024) evidence the relevance of this variable in explaining SDG implementation at the local level.

Other socio-economic indicators like the GDP per capita, GDP growth rate, inflation rate, unemployment rate, etc. (Guillamón et al., 2025) cannot be introduced as control variables because they have been used to calculate the SDG index, our dependent variable. Introducing any of these indicators into the model as explanatory variables would, therefore, bias the results.

In Table 1, the mean value of SDG is 72.29, indicating a medium-high level of SDG achievement, considering 100 as optimal performance. The maximum value of the whole sample is 85.60, achieved by Sweden in 2017, while Cyprus had the minimum value of 55.00 in 2019. The average of the SDG index has remained relatively unchanged during the period of analysis (2015–2022), as Figure 1 shows. However, there are relevant differences among countries across Europe. Figure 2 shows the average value of the SDG index in the analyzed time period (2015–2022) for each country. Overall, the northern European countries perform better than their southern counterparts, particularly Hungary, Bulgaria, Romania, and Cyprus.

Regarding the WGI variables, the quality of governance in Europe is medium-high since the mean values of the six indicators are around 1 (taking into account that each WGI takes values between −2.5 and 2.5). WGI_ps, which refers to political stability, has the worst mean value (0.60), while WGI_rq, representing regulation quality, has the best (1.05). This situation remains stable throughout the period, as Figure 3 illustrates. However, from 2019 onwards, there is a slight change: the mean value of regulation quality (WGI_rq) declines, while that of voice and accountability (WGI_va) shifts rises. A significant reduction in the average value of political stability (WGI_ps) in 2022 can also be observed due to the Russian-Ukrainian conflict. Indeed, the WGI_ps score of Ukraine in 2022 was −2.0 (the minimum). The northern countries show the best governance quality (maximum values): Norway stands out in “Voice and Accountability” (1.77), Finland in “Regulation Quality” (2.05) and “Rule of Law” (2.08), Iceland in “Political Stability” (1.64), Switzerland in “Government Effectiveness” (2.05), and Denmark in “Control of Corruption” (2.40).

Regarding the control variables, the data show that about 30% of the countries are governed by left-wing parties. The average percentage of seats in the legislature held by the governing party is 36.20%, suggesting that European governments tend to govern in coalition or, at the very least, need the support of other parties in the legislature. The financial balance is not very positive, as the mean value of Balance (−1.81) indicates a deficit situation. This result is strongly affected by Ukraine’s severe financial difficulties. Beyond this, Spain and Greece show the worst financial health. Finally, 38.42% of the population falls within the age range of 20–34; Greece and Italy have the lowest percentages, while Cyprus has the highest.

Table 2 shows the bivariate correlation between the explanatory variables. The WGI variables are highly correlated, confirming the results by Kaufmann et al. (2010), who noted that the six dimensions of governance are not independent. This is why the j WGI variables are included in the model individually to examine the effects of the different features of governance quality on SDG implementation. The rest of the coefficients are below 0.5, the “rule of thumb” for multicollinearity (Wooldridge, 2010).

Table 3 shows the empirical results of the model. There are six equations, one for each governance indicator, but the dependent variable (the SDG index) is the same in all of them. The dependent variable takes values between two limits (0 and 100), so it can be considered a censored variable. Therefore, the Tobit estimator is used to estimate the six equations.

All the governance indicators are positively related to the SDG index and statistically relevant, except WGI_ps. These findings suggest that, apart from political stability, the various features of governance quality have a positive effect on SDG implementation.

Regarding the control variables, findings that emerged from the analysis document that political factors (ideology and government fragmentation) and Young are not statistically relevant in most of the equations. This suggests that these factors do not appear to impact SDG implementation. Conversely, Balance is statistically significant, highlighting the positive role of positive financial condition. Finally, Population also has a positive effect on certain SDGs.

Several analyses have been conducted to check the robustness of our findings. Firstly, in addition to the analysis of overall SDG performance, scrutinizing different SDG dimensions (economic, social, and environmental) may reveal interesting patterns. Therefore, the SDG index has been disaggregated into four subindices according to the SDG “Wedding cake” approach. This model conceptualizes the interconnections between the SDGs and the dimensions of sustainability by showing the biosphere as the foundation of economies and societies (Obrecht et al., 2021). This model groups the 17 SDGs as follows:

1. Biosphere: including SDG6, SDG13, SDG14, and SDG15.

2. Society: including SDG1, SDG2, SDG3, SDG4, SDG5, SDG7, SDG11, and SDG16.

3. Economy: including SDG8, SDG9, SDG10, and SDG12.

4. Partnership: including SDG17.

While there are other conceptualizations, such as “the triple-bottom-line” (Elkington, 1997) or the “5 Ps” (UN, 2015), the “Wedding Cake” framework implements a hierarchy and asserts that building from the base up allows actors to harness operational efficiencies (Aubrecht, 2022). Although this model has some shortcomings, it has been used by the United Nations to promote the 17 SDGs (Winiwarter, 2020).

Six regressions for each “Wedding Cake” dimension were estimated, resulting in 24 equations. Including many equations in a single table would make it difficult to read the results, so we provide a summary table with the coefficients of the main variables ^[3] (WGIs). Table 4 shows that the WGIs are only statistically significant in the Economy and Society equations. These additional findings illustrate which set of SDGs plays a major role in the current scenario. The Society and Economy dimensions are more central, as documented in recent studies (e.g. Adebayo et al., 2025), while environmental aspects have been sidelined (e.g. changes in CSRD compliance, which means scaling back the reach and impact of the landmark EU Green Deal).

The second robust analysis addresses the causality between governance quality and SDG progress, which may introduce endogeneity issues in our models. The Tobit estimator cannot control endogeneity; the only estimators that can provide this control belong to the family of instrumental variables (IV). Although IV estimators are typically more suitable for continuous dependent variables, other studies (e.g. Guillamón et al., 2025) have applied this technique to the SDG index, which is a censored variable. We adopt the two-step system estimator by Arellano and Bover (1995), which uses lagged values of the right-hand side variables included in the model as instruments ^[4], so it uses more instruments than the traditional IV estimator. Table 5^[5] presents the results of the same six equations. They are similar to those obtained previously; all the governance indicators are positively related to the SDG index and statistically relevant, except WGI_ps. These findings suggest that the higher the quality of governance (in terms of accountability, regulation quality, rule of law, government effectiveness, and control of corruption), the better the SDG performance.

The third robustness-checking analysis considers the effect of the COVID-19 pandemic, which affected many indicators included in the preparation of the SDG index (e.g. People at risk of income poverty, Life expectancy at birth, Mortality rate, Individuals who use the internet to make appointments with a practitioner, the Gini Coefficient, etc.). Table 6 shows the results of the same six equations previously estimated, but considering an additional variable, Pandemic, which is a dummy variable that takes the value 1 for the years 2020, 2021, and 2022. In addition, the six equations include the interaction term between this new dummy variable (Pandemic) and the six WGIs. Again, all the governance indicators are positively related to the SDG index and statistically relevant, except WGI_ps. These findings suggest that the higher the quality of governance, the better the SDG performance. The variable Pandemic is positively associated with SDG achievement levels, indicating that European countries worked hard and successfully during the pandemic to implement the SDGs. However, the interaction effects are not statistically relevant in any case. This means that the positive nexus between governance quality and SDG achievement was not affected by the unfavorable context of those years. The positive effect continued despite the global pandemic.

Finally, the fourth robustness analysis is related to the conflict between Russia and Ukraine, which has affected all of Europe. It has been argued that the lack of statistical significance of the WGI_ps variable (representing political stability and the absence of violence/terrorism) could be due to the Russian-Ukrainian war. To control for this situation, we have estimated the six regressions by removing the years 2022 (when the war began) and 2021, as tensions were already present between the two countries during that year. Table 7 shows the results. As can be observed, WGI_ps is statistically significant and positively related to the SDG index, as are the other WGIs. Therefore, we can maintain that governance quality is essential to promoting and implementing the SDGs.

Regarding the control variables, the results in all the mentioned tables are similar to those obtained previously. That is, the two political factors (Left and Strength) and Young are not significant in most of the equations, while Balance and Population are positively linked to the SDG index.

Findings that emerged from the analysis document the positive role played by the various dimensions of governance quality on the implementation of policies aimed at achieving the various SDGs. Following the policy process framework (Bryson et al., 2006; Siddiki, 2022), effective governance involves implementing complex and interactive processes through which society and the economy can pursue common goals (Ansell and Torfing, 2022; Siddiki, 2022).

Our findings also align with the empirical results collected in other continents (Knox and Orazgaliyev, 2024; Adebayo et al., 2025). On a more granular level, they highlight the relevance of accountability, a pillar of governance quality that helps citizens understand how sustainability policies have been operationalized and implemented (Ríos et al., 2024; Trautendorfer et al., 2024). Similarly, the findings show that the capacity of governments to formulate and implement effective policies is a relevant factor. Scholars observed and the policy process framework supports, achieving the SDGs requires concentrating on effectiveness rather than efficiency (Ansell and Torfing, 2022; Sidiki, 2022). Our results also document the positive impact of other components of governance quality, indicating the relevance of regulation quality, the rule of law, and the fight against corruption. This result is coherent with what has emerged from the previous literature (Murphy and Albu, 2018), supporting the “sand the wheels” hypothesis (Ahmed and Anifowose, 2024). It is noteworthy that the various robustness analyses conducted have substantially yielded consistent results.

The only factors that seem less relevant than expected are political stability and the absence of violence/terrorism. A possible explanation for this is the above-mentioned situation in Ukraine, as one of the robustness analyses shows. More broadly, this result may be attributed to the European context investigated in this study, where the presence of ethnic, religious, or regional conflicts is not perceived as a concrete threat (Adebayo et al., 2025; Knox and Orazgaliyev, 2024; Musah, 2024).

Our results are also consistent with other studies that find no differences among generations (Deal et al., 2010; Lyons and Kuron, 2014; Magni and Manzoni, 2020). Specifically, for intrinsic work value and pro-environmental behavior, the younger generation was not more pro-sustainable than the older generations (Yamane and Kaneko, 2021). In fact, rather than generational differences, other combined factors such as environmental self-identity and higher education levels influence pro-sustainable behaviors (Ajibade and Boateng, 2021).

Furthermore, our findings document that a positive link exists between SDGs and financial health, which aligns with the policy process framework (Siddiki, 2022). Prolonged difficult financing conditions seriously undermine the prospects for SDG investment (UN, 2024). However, countries with good financial health can integrate the SDGs into their decisions and allocations. They can align spending with the SDGs and improve their effectiveness, as the UN demands (2024) and Ziolo et al. (2021) point out.

Finally, the fact that political factors, such as ideology and government fragmentation, are not statistically significant in the majority of the equations indicates that the SDGs hold general relevance and are incorporated into the political agendas of all governments, independent of their ideological stance. Bisogno et al. (2023) note that the political orientation of local governments does not affect the commitment to and implementation of SDG policies. At the national level, there is a consensus among European countries of all political ideologies that integrating 2030 Agenda policies can rationalize policy-making by eliminating contradictions and achieving synergies (Bornemann and Weiland, 2021), as the policy process framework maintains (Anyebe, 2018; Siddiki, 2022).

This research aims to contribute to the SDG literature, which investigates its implementation in different contexts. Understanding the driving forces that may hinder or favor SDG achievement is pivotal to implementing effective policies and making further progress toward sustainable development.

Previous studies investigating governance quality and the SDGs have mainly concentrated on particular contexts, especially developing countries, or they have focused on specific SDGs. Our study contributes to this emerging stream of literature by adopting a broad perspective that avoids focusing on specific goals. This study also sheds light on the European context, investigating a large sample of 35 countries from 2015 to 2022 by creating a panel dataset comprising 245 observations. To the best of our knowledge, this is the first study adopting a comprehensive analysis of governance features to examine how they can affect SDG achievement. Finally, our study has operationalized the complex concept of governance using Kaufmann et al.’s (2010) model.

The findings that emerge from the analysis advance the current literature investigating the relationship between good governance quality and the SDGs, in addition to offering valuable lessons for politicians and public officials.

From a theoretical perspective, our study contributes to understanding how the interaction of different governance elements in varying conditions, including a framework of governance quality, can support the sustainable development proposed by the 2030 Agenda. The variables analyzed align with our theoretical approach, offering an opportunity for further reflections by scholars. We find that accountability is a pivotal mechanism in this context. By fostering transparency and responsibility, accountability helps build widespread awareness of the sustainable goals and the collective actions required to achieve them. It serves as a tool to track the contributions of citizens and private and public institutions and organizations to a shared vision of a more sustainable society and future.

This study yields several important implications for policymakers and elected officials. First, there is a clear need to prioritize outcome-oriented policy interventions underpinned by robust accountability frameworks to effectively advance sustainable development objectives. Second, the findings underscore the importance of fostering cross-sectoral and intergovernmental collaboration – including partnerships among various tiers of government, the private sector, public institutions, and non-governmental organizations – to pursue shared developmental goals. Crucially, the development and adoption of common standards and benchmarks for policy implementation and evaluation can enhance transparency, facilitate comparative assessments, and contribute to more consistent and accountable governance practices across jurisdictions. The evidence presented can inform the design and advocacy of comprehensive, sustainability-focused policy strategies that address multifaceted societal challenges such as poverty, health and gender inequalities, educational disparities, and climate change. Politicians, by virtue of their representative mandate, are uniquely positioned to enact targeted legislation that enhances policy coherence, reinforces public trust, and encourages civic participation. From a policymaking perspective, the results further emphasize the necessity of institutional reforms that strengthen governance stability, regulatory quality, accountability mechanisms, and anti-corruption measures.

Another critical insight is the need to prioritize effectiveness over efficiency. While efficiency focuses on minimizing resources for given outputs, effectiveness emphasizes achieving meaningful and impactful outcomes in line with communities’ needs and expectations. Policymakers and public administrators should, therefore, direct their efforts beyond outputs to ensure the desired outcomes that align with broader societal goals.

This study has several limitations. From a theoretical perspective, some additional variables could be tested to reinforce the polyhedric analysis further. In addition, the research is limited to observing a group of countries, as it focuses on the European context. However, this limitation may also provide an opportunity for academics to develop a comprehensive understanding of how different components of governance quality contribute to global sustainability. Therefore, future research could investigate the governance-SDG nexus in other continents and add additional variables related to social, economic, and environmental conditions to provide additional insights and highlight the roles of context-specific factors.

This study is funded by the Ministry of Science and Innovation, Spain, under Project GELESMAT (PID2021-122419OB-I00).