Goals and Pathways of Public Governance Contribution to Achieve Progress in the Quality of Life

Abstract

This research examines the importance of effective public governance in maximising the well-being of citizens in EU member states. Our research strategy used an integrated modelling framework involving data mapping and an autoregressive model with distributed lags (ARDL) for 2012–2022 in the EU member countries. The results demonstrated that the World Governance Indicator (WGI) has a mixed impact on Quality of Life (QL). In the short term, the impact is positive if there is an increase in the level of the public governance indicator, and in the long term, the effect of public governance on the quality of life is negative due to the challenges associated with implementing reforms. Additionally, our results highlighted that, to improve citizens’ quality of life, policies should focus on increasing the Purchasing Power (PP) and Safety of Society (SS) as well as on issues related to Pollution (P) and the Cost of Living (CL). There are similarities between the long-term ARDL analysis estimate and the short-term one, with the latter pointing out that they will be able to have quick positive effects, while pollution and increased living costs have immediate adverse effects on quality of life.

1. Introduction

This study investigates the influence of public administration on improvements in the standard of living within the European Union (EU 23), employing Autoregressive Distributed Lag modelling (ARDL). In the face of a constantly changing world, analysing the challenges of public governance in maximising citizens’ well-being represents a vibrant picture of social and political complexity, providing key insights for a more prosperous and equitable future [1,2]. The topic of our research is crucial for analysis for two reasons. First, public governance plays an essential role in ensuring the well-being and prosperity of citizens through the implementation of appropriate policies and regulations [3]. Therefore, understanding the challenges governments face in this process is essential for improving citizens’ quality of life. Second, in the context of continuous social, economic, and political changes, governments face complex challenges, such as poverty, inequality, access to social and health services, and the environmental impact on well-being [4]. Moreover, in an increasingly interconnected and globalised world, there is a growing need for collaboration and exchange of best practices between governments to address common challenges and maximise benefits for citizens. Therefore, exploring this subject can contribute to a better understanding of the dynamics of public governance and how it can be optimised to promote well-being and social progress [5]. However, political instability and fiscal inefficiencies continue to impede progress. Research indicates that a balanced fiscal policy, which aligns revenues and expenditures, is essential for sustainable development and economic stability [6].

A robust body of theoretical and empirical research indicates that the quality of public governance is a key determinant of sustainable improvements in the quality of life for citizens [6,7]. Numerous academic studies have examined the relationship between public governance and quality of life, analyzing this connection in various contexts with respect to methodology, data, countries, time periods, and findings.

Moreover, public governance is considered an important factor influencing the quality of life by significantly impacting the allocation and efficiency of public resources [6].

Furthermore, as a country’s economy develops, people tend to place greater value on quality of life and life expectancy, and consequently have higher expectations for public services and governance in terms of efficiency, quality, sustainability, and also on a balanced budget for ensuring economic stability [6,8].

Building upon the key findings from the academic literature and the research objective of the study in this subsection, we also recognize a number of limitations of our own study. First, the analysis is limited to the 23 EU member states, and the findings may not be generalizable to other geographical regions. Second, the study focuses on the overall quality of public governance and its impact on quality of life but does not delve into the nuances of specific governance dimensions and their respective influence on various aspects of quality of life.

Given the disparities among the European Union’s member states in governmental quality, public governance, allocation of public resources, and public spending, as well as targeted initiatives to enhance well-being and economic growth, can have a significant impact on the quality of life of citizens, in both less developed and developed countries, demonstrating the significant contribution of public governance to quality of life.

This research aims to analyse the challenges of public governance in maximising citizens’ well-being, focusing on identifying and evaluating the variables that directly or indirectly influence society’s well-being. Possible solutions and strategies for addressing these challenges will also be analysed to improve the governance process and increase citizens’ satisfaction and happiness.

Understanding the foundations of this relationship can inform decision-making processes in relevant public policy domains, which can subsequently impact the quality of life for citizens. Furthermore, this research is designed to enrich the academic literature with an updated, comprehensive, and integrative evaluation of the relationship between public governance and quality of life, through the application of various econometric methods and techniques to ensure robust and precise results that provide the foundation for conclusions and recommendations for policymakers regarding public policy.

This study builds upon and integrates various theoretical perspectives, highlighting the crucial role of public governance quality in advancing the quality of life through its multifaceted nature. Furthermore, it identifies the essential mechanisms, policies, and strategies that must be designed and implemented within a flexible and coherent framework to support the well-being of citizens, with positive implications for the overall well-being of society.

The study’s novelty comes primarily from its contribution to its distinctive approach to empirical analysis and the development of the research framework. Advanced techniques and research methods, including panel data econometric analysis, content analysis, and bibliometric analysis, have enabled a more in-depth examination of the relationship between innovation and economic development within the European Union member countries. This innovative approach enables us to capture the obvious aspects, subtle interdependencies, and mutual influences, providing a comprehensive and detailed perspective.

Therefore, the subject of our research represents a current issue based on the difficulties and challenges that public governance encounters in its path to maintain and develop the well-being of society and the quality of life of citizens. Considering the socioeconomic context in which the countries of the European Union find themselves (financial and energy crises, pandemics, conflicts with other states, lack of trust in public authorities, inflation, etc.), our research presents a timely and beneficial study for the field of public, political, cultural, or religious finances. In this complex framework, the present study focuses on determining the influence of public governance on quality of life, starting from the hypothesis that public governance can positively affect quality of life. The research hypotheses developed in this study, based on a critical analysis of the literature and the overall objective addressed in this paper, are as follows:

Hypothesis 0 (H0): 

There is a significant long-term direct and/or indirect relationship between the quality of public governance and the state of quality of life.

Hypothesis 1 (H1): 

There is a significant short-term relationship between public governance and progress in the quality of life.

Specifically, the study aims to investigate the relationship between various aspects of public governance, such as Voice & Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption, and their impact on the overall well-being. The empirical findings support the general equilibrium model and point out particular effects of public governance on life quality.

In this paper, we aim to explore all EU-23 member states over 10 years, from 2012 to 2022, to analyse how public governance can respond effectively and efficiently to these challenges. By examining case studies, comparative analyses, and critical evaluations of existing policies, we will seek to identify innovative strategies and approaches that sustainably and equitably maximise citizens’ well-being. Therefore, we will focus on identifying critical aspects of public governance that influence citizens’ well-being.

This research paper is divided into two parts: a theoretical part and a practical one. The theoretical approach includes a bibliometric analysis, which allowed us to map the main countries, authors, and most used keywords in our research area. The analysis covered the period from 2010 to 2024. The bibliometric analysis enabled us to identify the most relevant data amidst the extensive literature.

The second part of our research paper, which includes the empirical analysis, specifically presents the research objective of this study, which is to identify the impact of public governance on citizens’ well-being. We applied various methodologies such as data mapping and the ARDL (Autoregressive Distributed Lag) methodology to determine which countries are most important from the perspective of governance quality and quality of life, the long-term and short-term relationships between the two economic variables, and their main challenges. The final part of the research presents the main results and conclusions obtained.

Besides the introduction, our research paper consists of the following parts: Section 1 provides a brief preface on the relationship between public governance and the challenges it faces to maximise citizens’ well-being. Section 2 presents a detailed review of the specialised literature. Section 3 is dedicated to describing the economic research variables, the presentation of the applied methodology, and the main results of applying these types of empirical analysis. Finally, Section 4 and Section 5 present the discussions and conclusions derived from this work. The theoretical part of the paper contains Section 1 and Section 2, while the second part, the empirical analysis, is presented in Section 3.

By addressing the complexities of public governance and its impact on citizen well-being, this study aims to contribute significantly to the ongoing public administration and policymaking discourse, offering new perspectives and practical solutions for enhancing governance effectiveness in the EU.

2. Literature Review

Over the past decades, the question of whether effective public governance can positively impact the long-term quality of life for citizens has attracted significant attention from academics, policymakers, and the general public. This study examines this important relationship from the perspective of European policy uncertainty, analysing the complex connection between various aspects of public governance and their influence on overall quality of life. The quality of public governance [9] plays a crucial role in this, directly influencing the quality of life through the measures it implements to solve everyday problems such as pollution, traffic, health, public safety, and purchasing power. According to [10], the Quality of Life Index, which is an indicator of well-being, includes factors such as purchasing power, pollution, housing price relative to income, cost of living, safety, healthcare, traffic, and climate, showcasing the multidimensionality of computing an indicator for assessing well-being. Moreover, the OECD [11] uses the Better Life Index to measure well-being, focusing on essential factors such as housing, income, employment, community, education, ecosystem, social involvement, health, life satisfaction, safety, and harmony between work and personal life. Therefore, not just one indicator tries to measure well-being, making it more difficult to pinpoint how to measure well-being and societal progress accurately [12].

Public governance is defined as how governments, together with social institutions and organisations, interact with citizens and make decisions in a complex context [13]. Studies show that the involvement of governments in various fields, from the economy to health and the environment, is crucial for maintaining and improving social well-being [14,15,16]. Ref. [17] describes governance as a set of traditions and institutions through which authority is exercised in a country, including the selection and monitoring of governments, the ability to formulate and implement policies, and respect for institutions that regulate economic and social interactions. According to the OECD, governance indicators focus specifically on independence, social responsibility, and scope for action in key sectors. In a democratic political framework and a free-market economy context, the private sector significantly influences societal well-being by applying public law. Legislative changes can directly and indirectly affect citizens, such as increased taxes [18], leading to more expensive products and services, thus affecting consumers. Measures such as price caps or VAT reductions [19] directly impact well-being by influencing the cost of living. The quality of governance is also related to a country’s economic development level; poorer countries tend to have lower-quality governance, while richer countries benefit from more efficient and professional administrations [20,21]. Equality-oriented management in meeting socio-professional needs and improving the capacity to implement measures are essential for public governance and societal well-being. Government decisions are influenced by factors such as profitability, sustainability, and political and monetary costs, which determine the feasibility of the measures adopted. Moreover, technological advances provide both opportunities for public governance.

The challenges of public governance in maximising citizen well-being have been extensively addressed over time. These challenges will always persist in various forms and will impact the European Union member states. Although they are aware of both existing and potential challenges, each member state addresses and anticipates these issues differently, given the broad diversity of factors that can influence the emergence of new challenges and their approach and resolution.

Given the complexity and vast scope of the literature on public governance and citizen well-being and the need for society to receive high-quality services in return for their financial contributions to the public budget, we decided to assess the current state of knowledge in this field. Our objective was to conduct a detailed analysis that could serve as a foundation for future research on the relationship between public governance and citizen well-being. By applying the bibliometric analysis, we can gain a comprehensive overview of the evolution of our research topic, thereby pinpointing the most influential works and primary directions. This approach facilitates structuring the existing scientific discourse and identifying gaps in the literature that warrant further investigation.

Implementing bibliometric analysis is a rigorous and organised process of evaluating, synthesising, and interpreting selected data or information to identify significant patterns, trends or relationships through specific methods and techniques so that the available information is systematically examined for reaching conclusions or recommendations objectively and reliably. We can exclude unclear, uncertain, or confusing results through the bibliometric analysis, which is why we considered this analysis more suitable for carrying out our study, to the detriment of the traditional one, the latter being more subjective and less structured.

We employed a bibliometric research method to document this thoroughly and recognise significant contributions in the field [22]. This analysis was facilitated using VOSviewer 1.6.19 software, known for its ability to visualise bibliometric networks clearly and intuitively. The VOSviewer software is recognised for its ability to visualise bibliometric networks clearly and intuitively. This software is distinguished by its advanced mapping and clustering functions, which allow detailed exploration of citation and co-authorship networks. Compared to other bibliometric analysis software, such as CiteSpace or Gephi, VOSviewer offers a more user-friendly interface and facilitates the manipulation and interpretation of complex visualisations. In addition, its clustering algorithms are very effective in identifying research communities and thematic links. Through the software, we were able to categorise all studies relevant to our research area. At the same time, it should be mentioned that the VOSviewer software works exclusively in English, which requires using terms specific to this language in our analysis.

Also, we used the Web of Science Core Collection platform to extract the necessary database, with the preliminary step involving the keywords “Public Governance” and “Well-being”. Ultimately, after applying inclusion/exclusion filters, we obtained 140 articles, which were saved in a .txt” file that we imported into VOSviewer for appropriate data analysis according to the following steps:

• Web of Science—Clarivate (Analytic database):

  -

  The subject of the research: “Public Governance” and “Well-being”;

  -

  Period: 2010–2024;

  -

  Documents type: “Article“;

• VOSviewer Instruments:

  -

  Recorded content for VOSviewer: Full text and cited references.

• The analysis was based on:

  -

  keywords;

  -

  citations/countries;

  -

  citations/authors.

Following the application of certain filters, a relatively small number of results, more precisely 35 papers, resulted, so we decided to widen the research area; thus, we considered only “Well-being” as a keyword, without including the term “Citizens.”

We initially yielded a set of 603 global results, published between 1995 and 2024, dealing with our research topic. However, we filtered the data so that only journal articles were retrieved, resulting in 514 articles. In order to perform a relevant and current analysis, we reduced the time period to 2010–2024, which reduced the number of articles to 483. Regarding the accessibility and relevance of the results, we restricted the articles to only those in English, resulting in a number of 455.

A final filter we used in the analysis to exclude papers that do not fit into our research area was to select only documents that included the study of one or more countries/regions in the European Union. Finally, we limited the analysis to 140 documents considered relevant, and following their evaluation, we extracted only those articles that presented at least one influencing element on our research. Thus, following the application of the inclusion/exclusion parameters, we extracted only articles, resulting in 10 articles, which we included in the elaborated analysis. The previously described step-by-step process can be seen as more straightforward and structured, employing the following figure (Figure 1).

The keyword analysis highlights the most important keywords and the connections among them. The main objective of this is to provide us with an overview of the frequency of use of the most adopted keywords by authors in our field of research. As a keyword and node become more prominent, the weight of the keywords increases. Additionally, the smaller the distance between these nodes, the closer and more significant the relationship among them.

Figure 2 highlights the most important keywords and their connections. The first and most representative cluster (red) is led by the keyword “Governance”, which is one of the main topics of our analysis, followed by terms such as “innovation”, “e-government”, and “sustainable development”. The second group (green) includes nine keywords, such as “citizens”, “climate change”, and “public health”. The third cluster (blue) contains keywords like “COVID-19”, “Europe”, “government”, and “implementation”. The fourth cluster (yellow) features words such as “biodiversity”, “challenges”, and “sustainability”. Lastly, the final group includes keywords like “well-being”, “participation”, and “public services”. These groups are highlighted in

Table 1. Groups of keywords.

Group 1 (Red)	Group 2 (Green)	Group 3 (Blue)	Group 4 (Yellow)	Group 5 (Purple)
Democracy	Attitudes	Covid-19	Biodiversity	Care
E-government	Citizens	Government	Challenges	Co-Production
Empowerment	Climate change	Implementation	Cities	Coproduction
Governance	Framework	Institutions	Ecosystem Services	Participation
Inequalities	Health	Performance	Green Infrastructure	Perspective
Innovation	Perceptions	Policy	Land-use	Public-services
Knowledge	Public health	Politics	Management	Well-being
Networks	Public-health	Satisfaction	Resilience
Organisations	Quality	Europe	Sustainability

, encompassing all the keywords.

In other words, Group 1 reflects the idea of managing and leading society in a transparent, responsible, and participatory manner, considering the current and future needs of citizens and the surrounding environment. The keywords in Group 2 are related to how citizens perceive the implications of climate change on health, as well as the quality, policies, and institutional frameworks that govern the field of public health. The set of keywords in Group 3 is related to how governments and European institutions implemented policies and measures during the pandemic, the evaluation of these institutions’ performance and the adopted policies, the impact of these policies and political decisions on public satisfaction, and the outcomes of these actions in combating the pandemic and maintaining citizen satisfaction. Furthermore, Cluster 4 suggests the management of biodiversity and ecosystem services in urban environments, addressing challenges related to urbanisation and land use sustainably, promoting green infrastructure and city resilience in the face of climate change, and encouraging sustainable development that balances human needs with the conservation and protection of the surrounding environment. Finally, the last cluster reflects the importance of community involvement in providing, evaluating, and managing public services and their perspective on these services and overall well-being.

The “scientific co-authorship” analysis explores the network of leading authors. Figure 3 presents a map of five clusters, with seven authors out of 567 included in the study. Only authors who have indexed at least two articles on the Web of Science platform and have recorded at least one citation were considered.

Thus, VOSviewer provides the following information about the most cited authors: all authors have produced at least two documents, except for Țăran A. and Coomans J., who have authored three documents. The first group (red) and the second group (green) are the main ones, as they are notable for containing two authors each and for their strong connections, with a strength greater than 1.

Table 2. Groups of papers and citations by the author.

Group	Authors	Doc./Cit.	Binding Strength
Group 1 (Red)	Alves, Fatima	2/6	1
	Leal Filho, Walter	2/9	1
Group 2 (Green)	Lobont, Oana-Ramona	2/1	2
	Taran, Alexandra-Mădălina	3/1	2
Group 3 (Blue)	Coomans, Janna	3/12	0
Group 4 (Yellow)	Mustalahti, Irmeli	2/101	0
Group 5 (Purple)	Sacchetti, Silvia	2/2	0

provides more detailed information about the authors, including the number of documents and citations and the strength of the connections.

We can observe that Mustalahti I. (yellow) has recorded the highest number of citations, with 101 citations, making it the most relevant group from the citation perspective. The next most impactful in terms of citations are Coomans J. (blue), with three published documents and 12 citations and Leal Filho W. (red), with two published documents and nine citations. Also in the red group is Alves F., with two documents and six citations. Regarding the strength of connections, Țăran A.M. and Lobonț O.R. (green) show the highest values in this regard, with three and two published documents, respectively, and one citation each.

The final stage of the bibliometric analysis focuses on examining scientific co-authorship in terms of the volume of documents and citations compared with the countries studying our research topic. We used a sample of 140 articles considered in the systematic analysis, considering only those that meet the following criteria: at least five documents per country, and at least 40 citations per country.

Figure 4 represents the map of this analysis, which helps to highlight the connections and collaborations between authors from different countries. Therefore, the map illustrates the level of interconnection between the analysed countries, highlighting those with significant roles in the spheres of governance and the well-being of their populations. As shown in the figure below, six clusters of different colours have emerged, with significant connections among them. We can state that the USA, Germany, the Netherlands, Australia, and the UK are the most dominant countries in this analysis, having the most prominent and most robust nodes.

Table 3. Article groups by country.

Group	Authors	Doc./Cit.	Binding Strength
Group 1 (Red)	Canada	7/378	34
	Germany	21/353	45
	Portugal	9/305	23
	Romania	8/59	2
	South Africa	5/214	16
	Switzerland	6/235	15
Group 2 (Green)	Australia	15/490	53
	Austria	5/57	5
	France	11/93	17
	Wales	5/134	22
Group 3 (Blue)	Denmark	9/180	20
	Finland	7/201	16
	Spain	21/268	20
	USA	20/431	57
Group 4 (Yellow)	England	23/576	61
	Italy	26/251	13
	Poland	7/40	1
Group 5 (Purple)	Belgium	5/134	5
	Netherlands	23/591	42
Group 6 (Turquoise)	Sweden	10/93	21

provides a more thorough overview of the grouping based on articles.

The results indicate that the geographical area in which the discussed topics were analysed is extensive. Thus, the results of the analysis are complex, and we can observe strong connections between countries from various parts of the world. As shown in Table 3, the main influencing countries in the field of public governance analysis and its impact on citizen well-being are Italy (26 documents), the UK (23 documents), the Netherlands (23 documents), Germany (21 documents), and Spain (21 documents), which are the countries with the highest number of articles published on this topic. In terms of citations, the most significant number of citations are found in countries such as the Netherlands (591 citations), the UK (576 citations), Australia (490 citations), and the USA (431 citations). It is noticeable that most of these countries are EU member states, which indicates the increased relevance of the EU area in which the study was conducted.

The bibliometric analysis conducted in addition to the classical literature review highlights the considerable research interest in the challenges of public governance and maximising citizen well-being in European Union countries. The level of communication among various countries is significant, reflecting a common interest in this field.

Even according to the analysis of scientific co-authorship in terms of the number of documents and citations reported to countries, research in the EU presents a significant base and a growing concern for this subject. Additionally, there is an increased desire to improve outcomes regarding quality of life and citizen satisfaction, with public decision-makers focusing on implementing policies that ensure effective governance and societal well-being.

The main points discussed in the specialised literature underline that progress in public governance and well-being policies is essential for improving citizens’ quality of life.

Studies show that although there are controversies regarding the effectiveness of governance measures on citizens’ well-being, most authors focus on these measures’ positive and negative aspects. According to the analysis, there is clear evidence that effective governance can significantly improve citizens’ well-being, but implementing these measures requires continuous adaptation and strict monitoring.

In conclusion, the relationship between public governance and citizens’ well-being is complex and multifaceted, influenced by multiple variables and contextual factors.

The researched subject remains relevant, and interest in analysing the challenges of public governance and its impact on citizens’ well-being is growing, especially in the context of recent sociopolitical changes. The study suggests that effective, transparent, and citizen-oriented governance is crucial for ensuring high societal well-being.

Future research will explore additional variables related to well-being indicators, including technological advancements and innovations in governance. Expanding the study to compare EU member states with other global regions over an extended period will allow a deeper examination of the statistical link between governance and well-being. Moreover, a more comprehensive approach is required to analyse the various dimensions of governance and their direct and indirect impacts on citizens’ well-being.

3. Methodology and Data

3.1. Method and Methodology

Considering effective public governance as an imperative premise for maximising citizens’ well-being, the necessity of continuous improvement and constant investment in governance practices is emphasised. This would lead to an increased quality of life and overall well-being of citizens. Based on this hypothesis, two different methodologies were applied to group countries to obtain a ranking of European states based on specific criteria, as follows:

• Data mapping of Key Dimensions Using Microsoft Excel 16.88;

To highlight and rank the variables in our study model at the level of the EU-23 member states in 2022, following the method used by [22], the data mapping technique was employed for the two dimensions: Public Governance (WGI) and Quality of Life (QL) for the data collected in 2022.

2.

Analysis of Autoregressive Distributed Lag (ARDL) Models Using EViews 12 Software;

The ARDL (Autoregressive Distributed Lag) analysis is an econometric method used to examine long-term relationships and short-term dynamics between economic variables. This analysis was proposed by [23] and provides a robust framework for assessing the effectiveness of governmental policies in improving the quality of life in society. Compared to traditional approaches, the ARDL methodology offers significant advantages. This approach is beneficial for studies where time series data are integrated of different orders, meaning some variables are stationary (I(0)) (their levels do not depend on time) and others are incorporated (I(1)) (their levels need to be differenced once to become stationary). The ARDL model is robust enough to provide unbiased and efficient estimates even for small samples and endogenous explanatory variables. By applying such a rigorous methodology, we can better understand the complex relationships between public policies and societal well-being, thus providing a solid framework for making informed and effective decisions to maximise social well-being.

ARDL works by modelling an autoregressive equation that includes lagged terms of both the dependent and explanatory variables. The process involves testing for the existence of a long-term equilibrium relationship between variables using the bounds-testing approach. If a cointegration relationship is detected, the ARDL model allows for estimating both short-term and long-term adjustment parameters. A major advantage of the ARDL method is its flexibility in handling mixed time series (I(0) and I(1)), avoiding issues associated with pre-testing for the order of integration. Consequently, ARDL is widely used in economic research to assess the dynamic effects of economic policies, market fluctuations, and other economic shocks on variables of interest.

We used the EViews software, a platform specialised in managing, analysing, and visualising time series and cross-section data to apply this type of analysis. EViews provides an intuitive graphical interface with extensive visualisation capabilities, including customisable charts and reporting options, facilitating the communication of analysis results. The main role of EViews in the context of ARDL analysis is to provide a robust and specialised platform for modelling and analysing economic data.

Moving forward, we will apply the ARDL analysis model to investigate both long-term and short-term relationships between key variables, aiming to obtain relevant estimates concerning our research objectives in public governance and citizen well-being.

This analysis was conducted to investigate the long-term and short-term relationships between independent variables, in our case, the six governance indices that we have transformed into a composite indicator, labelled “Worldwide Governance Indicator” (WGI), as well as the dependent variable, namely the Quality of Life index (QL). For this type of analysis, we also needed to choose control variables; in our case, we selected the purchasing power index (PP), the Cost of Living Index (CL), the Safety Index (SI), and the Pollution Index (PI). We chose only these four indices from the seven subdivisions considered in our research of the composite quality of life indicator because they are deemed essential factors that influence well-being and quality of life, having significant implications for economic and social dynamics. Thus, we excluded the healthcare index, property price relative to income, and traffic index, as these may be less relevant or have a smaller influence on the dependent variable (QL). The period for which this analysis was conducted was 2012–2022, and the countries considered for this analysis were the same as in previous studies, namely EU23 member countries.

The first step in conducting this analysis is to test the stationarity of the time series. Thus, if the sequence is in a stationary process (I(0)) or non-stationary (I(1)), the ARDL cointegration test can be performed using the following Equation (1):

$$∆{QL}_t=a_0+{\sum }_{i=1}^n{a}_{1i}∆{QL}_{t-i}+{\sum }_{i=0}^n{a}_{2i}∆{WGI}_{t-1}+{\sum }_{i=0}^n{a}_{mi}{Control}_{t-1}+{\mu }_i$$

(1)

where:

$∆{QL}_t$ = the dependent variable at time “t” and represents the change (difference) in quality of life compared to the previous period;

$a_0$ = the model intercept, a constant coefficient representing the average value of the change in quality of life when all other variables are zero;

${\sum }_{i=1}^n{a}_{1i}∆{QL}_{t-i}$ = the sum of all autoregressive terms for the dependent variable (QL) at different lags (from 1 to n);

${\sum }_{i=0}^n{a}_{2i}∆{WGI}_{t-1}$ = an autoregressive term for the independent variable (WGI) at different lags (from 0 to n), representing the effect of governance delay on quality of life;

${\sum }_{i=0}^n{a}_{mi}{Control}_{t-1}$ = is the sum of all autoregressive terms for the control variables (PP, CL, SI, PI) at different lags (from 0 to n);

${\mu }_i$ = represents the random error at time t, specifically the unexplained variation in quality of life.

As for the estimation of the short-term effect, it is given by the following Equation (2) [24]:

$$∆{QL}_t=a_0+{\sum }_{i=1}^n{a}_{1i}{∆QL}_{t-1}+{\sum }_{i=0}^n{a}_{2i}{∆WGI}_{t-1}+{\sum }_{i=0}^n{a}_{ki}{Control}_{t-i}+{ECM}_{t-1}+{\mu }_i$$

(2)

We observe a modification to this equation, the appearance of a new term “${E,CM}_{t-1}$” which represents the error correction factor, indicating the speed of self-correction of the economic system.

3.2. Data

The dataset used in the case study analysis included indicators from both the field of Public Governance and Quality of Life. Data for public governance analysis were collected from the WGI—World Governance Indicators database [25], while data related to Quality of Life were extracted from sources provided by the Numbeo platform.

Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns of governance perception across different countries and over time. In this regard, we have taken from the WGI—World Governance Indicators database the six indicators that measure governance quality, which are described in the following

Table 4. Description of the Six Public Governance Quality Indicators.

No.	Indicator Name	Description	Unit of Measurement
1.	Voice and Accountability	Reflects the extent to which a nation’s residents are able to participate in the electoral process, including freedom of expression, association, and access to independent media.
2.	Political Stability and Absence of Violence/Terrorism	Assesses perceptions regarding the likelihood of political instability and/or politically motivated violence, including acts of terrorism.
3.	Government Effectiveness	Reflects perceptions of the quality of public services, the efficiency of the public sector, the degree of autonomy from political influences, the effectiveness of policy formulation and implementation, and the credibility of the government’s commitment to these policies.	These six indicators are measured according to World Governance Indicators (WGI) in standard units of a normal distribution, with a mean of 0 and a standard deviation of 1, ranging from approximately −2.5 to 2.5.
4.	Regulatory Quality	It represents perceptions of the government’s ability to develop and implement sound policies and regulations that promote and facilitate private sector growth.
5.	Rule of Law	Reflects perceptions of the extent to which members of society have confidence in and abide by the rules of society, including the quality of contract enforcement, property rights, police and judicial services, and the likelihood of crime and violence.
6.	Control of Corruption	Represents the extent to which public power is exercised for private gain, including petty and grand forms of corruption and the “capture” of the state by elites and private interests.

:

The six governance quality indicators are “Voice and Accountability”, “Political Stability and Absence of Violence/Terrorism”, “Government Effectiveness”, “Regulatory Quality”, “Rule of Law”, and “Control of Corruption”. All values are in the units of a standard normal distribution, with a mean of 0 and a standard deviation of 1, taking values from −2.5 to 2.5.

According to the Numbeo database, the indicators evaluating Quality of Life are constantly updated using data collected over 36 months, ensuring that the index is as current and accurate as possible. It is worth noting that due to a lack of data for 2012–2016 regarding the Climate Index, we decided to exclude it from our empirical analysis.

Table 5. Description of the Composite Quality of Life Indicator and Its Sub-Dimensions.

No.	Indicator Name	Description	Unit of Measurement
1.	Composite Quality of Life Index	This index assesses the overall well-being of a community using an empirical formula incorporating various indicators. It represents a composite index of the following: purchasing power, safety, healthcare, cost of living, property price-to-income ratio, traffic, and pollution.	Higher values are better
2.	Purchasing Power Index	Indicates a region’s or country’s economic power relative to a reference currency, typically the US dollar. This index reflects consumers’ ability to purchase goods and services based on available income and the cost of living in that region or country. Higher values indicate greater purchasing power.	Higher values are better
3.	Safety Index	Assesses the overall level of safety in a region or country, taking into account aspects such as crime rate, police presence, corruption levels, and other potential threats to personal and property safety. Higher values indicate a safer region or country for residents and visitors.	Higher values are better.
4.	Healthcare Index	Evaluates the quality and accessibility of the healthcare system in a region or country, including aspects like medical service quality, access to healthcare, medical infrastructure, and associated costs. Higher values generally indicate a better and more accessible healthcare system.	Higher values are better.
5.	Cost of Living Index	Reflects the overall level of the cost of living in a region or country by comparing the prices of basic goods and services (such as food, rent, transportation, and healthcare) to the average income of residents. Higher values indicate a higher cost of living, meaning living in that region or country is more expensive.	Lower values are better.
6.	Property Price-to-Income Ratio	They are used to assess housing affordability for residents of a region or country. This ratio compares the average price of homes to the average household income in the area. Higher ratios indicate that it is more difficult for people to afford housing, and housing affordability is lower.	Lower values are better.
7.	Traffic Index	Evaluates the average travel time duration in traffic in a region or city. This index reflects the average time people spend in traffic during their commutes or other daily activities. Higher values indicate longer average travel times and, consequently, greater traffic congestion.	Lower values are better.
8.	Pollution Index	Assesses the pollution level in a geographic area or country, including factors like air quality, water quality, and other forms of pollution such as noise and soil contamination. Lower values indicate better environmental quality in the area or country.	Lower values are better.

describes the composite Quality of Life Index and its seven sub-dimensions.

Regarding the composite Quality of Life Index (the unit of measurement is not clearly defined), according to Numbeo, the current formula used for calculating this index (expressed in Java programming language) is: index.main = Math.max [0, 100 + purchasingPowerInclRentIndex/2.5 − (housePriceToIncomeRatio × 1.0) − costOfLivingIndex/10 + safetyIndex/2.0 + healthIndex/2.5 − trafficTimeIndex/2.0 − pollutionIndex × 2.0/3.0 + climateIndex/3.0).

The fact that these indicators are also found in studies conducted by the authors we identified through manual selection for the bibliometric analysis, such as [26], who used government effectiveness, Refs. [22,27] who utilised these indicators of governance quality and quality of life, assures us of their validity and justifies their selection for our analysis.

We chose a 10-year analysis period covering the years 2012–2022, with a primary focus on 2022. This time frame includes a series of significant events that profoundly influenced public policies and governance approaches, as seen in Figure 5, which represents a timeline of major events from 2012 to 2022.

At the beginning of this period, the Eurozone economic crisis was felt, culminating in the sovereign debt crisis, which forced governments to implement strict austerity measures and structural reforms. The crisis demonstrated the consequences of fiscal mismanagement and the critical role of international cooperation in addressing economic challenges [28]. In 2015, Croatia became an EU member state, and the migration policy crisis [29] placed significant pressure on governance systems and integration policies. Additionally, the exit of the United Kingdom from the European Union [30] and the COVID-19 pandemic in 2020 can be noted. These events, among others, prompted a re-evaluation of governance strategies and spurred initiatives to strengthen social and economic cohesion within the European Union.

It is necessary to mention that, due to the lack of data for some countries on the Quality of Life Index, we decided to exclude the following states from the empirical analysis: Cyprus, Latvia, Luxembourg, and Malta. Additionally, as a result of Brexit in 2020, we decided to exclude the United Kingdom from the analysis area, ultimately resulting in a total of 23 EU member countries. Also, it is worth mentioning that, due to the lack of data for the period 2012–2016 regarding the Climate Index (a subdivision of the Quality of Life Index), we decided to exclude it from our empirical analysis.

4. Results and Discussions

4.1. Analytical Approach Involving Data Mapping

Figure 6 represents the six quality indicators of governance (WGI) for the year 2022, using the data mapping process with Microsoft Excel software for the 23 EU member countries included in the analysis. The aggregation of the Governance Quality Indicators, which range from −2.5 to 2.5, with higher values indicating better governance outcomes, reveals notable disparities among the EU-23 countries.

The leaders highlighted in the figure above are Denmark, Finland, and Sweden. This positioning is unsurprising, as these countries are considered high-quality governance states with competitive economies, efficient resource management, and progressive societies. These countries promote civic participation, tolerance, and social inclusion, ensuring political stability and social cohesion. Additionally, they stand out from other states due to their excellence in innovation and research, investing significantly in development and collaboration between the public and private sectors. These countries pay particular attention to environmental protection and sustainability, leading the way in adopting renewable energy and carbon emission-reduction measures.

The three Scandinavian countries are immediately followed by the Netherlands, Germany, and Austria and are recognised for their various contributions and achievements. For example, the Netherlands is known for its water management systems. At the same time, Germany and Austria are involved in environmental protection and conservation of natural landscapes, all contributing to a shared commitment to sustainability.

At the bottom of the ranking are Hungary, Croatia, Romania, Greece, and, in last place, Bulgaria. These countries are often considered to have high levels of corruption and issues with transparency and governance integrity compared to other European countries. Hungary, Croatia, Romania, Greece, and Bulgaria face well-documented cases of high-level corruption, limited access to government information, and opacity in political decisions. They also struggle with nepotism, favouritism in public contract allocation or government appointments, and difficulties in effectively utilising EU funds due to corruption, bureaucracy, or lack of administrative capacity, which has impacted the development and implementation of projects. Furthermore, inefficiencies in the judicial system, characterised by lengthy, ineffective judicial processes and a lack of judicial independence, affect the objective application of the law and allow corruption to thrive without adequate sanctions.

The composite quality of life indicator is represented in Figure 7, using the same sample of countries, namely EU-23, with a focus on 2022.

Denmark also ranks at the top here, along with the Netherlands, Finland, Germany, and Austria—countries with the highest values for this index due to interconnected factors. A central element is citizens’ access to high-quality healthcare services and advanced educational systems that guarantee quality and accessible education for all citizens. Additionally, robust social protection and low crime rates in these countries are other significant factors that generate a general sense of safety and stability. The economies of these countries are stable and diversified, offering well-paying jobs and professional development opportunities, while environmental policies are well-implemented and ensure a clean and sustainable environment. Developed infrastructure, efficient and accessible public transportation, equal opportunities, and social inclusion contribute to a high quality of life. Therefore, the combination of these factors—healthcare, education, social protection, strong economy, clean environment, developed infrastructure, social equality, and work-life balance—explains the high ranking of these countries in the Quality of Life Index. Thus, these countries are considered the highest performers in terms of both governance quality and quality of life.

At the bottom of the ranking are Romania, Bulgaria, and Greece, which face the lowest Quality of Life Index values. The reasons are due to several factors, such as limited and variable quality access of citizens to healthcare services, which is attributed to the underfunding of health systems in these countries, leading to difficulties in providing efficient and timely medical care, and thus creating inequalities in access to treatments and medical services. Moreover, education is a significant issue as these countries struggle to ensure quality and accessible education for all citizens. Social protection and safety are other problematic areas. Social protection programs are often inadequate, leading to high economic insecurity for the most vulnerable citizens. Additionally, relatively high crime rates contribute to a general sense of insecurity. The economies of these countries are less stable and diversified than those at the top of the ranking, and high unemployment rates and relatively low wages negatively affect the standard of living. Discrimination and inequalities persist in these countries, affecting equal access to opportunities and resources, while the work culture emphasises excessive overtime, impacting work-life balance. Considering these factors outlined above, the low Quality of Life Index values in Romania, Bulgaria, and Greece are explained.

4.2. Analysis of Autoregressive Distributed Lag (ARDL) Models Using EViews 12 Software

To examine the relationship between public governance and quality of life, we used the ARDL (Autoregressive Models with Distributed Lags) methodology, taking into account the data available for the period 2012–2022 at the level of the European Union EU-23. This methodological approach ensures the findings’ reliability and consistency while offering a comprehensive understanding of the relationships among the examined variables. The Autoregressive Distributed Lag model is an effective tool for investigating causal linkages and generating accurate statistical forecasts. It enables the identification of both the long-term and short-term effects of the variables and the assessment of the direction and significance of these relationships. Furthermore, the ARDL method provides the opportunity to control the variables, further enhancing the robustness of the analysis.

The Autoregressive Distributed Lag approach does not require all variables to be integrated in the same order, but the order of integration must not exceed I. Pesaran proposed that the critical value of the F-statistic be determined based on the order of integration of the variables, which can be either I (1) or I (0).

If the test statistic exceeds the critical value, the F-statistic used to assess the presence of a cointegration relationship will be invalid. This study utilises the Im, Pesaran and Shin test and the Levin, Lin and Chu test to determine the order of integration of the variables.

Table 6. Unit Root Test.

Im, Pesaran and Shin (IPS)-level
	t-statistic	p-value	conclusion
WGI	−0.48469	0.3139	Unstable
QL	0.04389	0.5175	Unstable
PP	0.16630	0.5660	Unstable
SI	−1.65694	0.0488	Stable
CL	−1.78751	0.0369	Stable
PI	−3.31071	0.0005	Stable
In first difference
WGI	−3.66358	0.0001	Stable
QL	−2.01760	0.0218	Stable
PP	0.16630	0.5660	Unstable
SI	−3.47880	0.0003	Stable
CL	−3.73960	0.0001	Stable
PI	−8.42438	0.0000	Stable
Levin, Lin and Chu (LLC)-level
WGI	−4.24853	0.0000	Stable
QL	−1.65431	0.0490	Stable
PP	−3.05945	0.0011	Stable
SI	−5.82266	0.0000	Stable
CL	−8.95770	0.0000	Stable
PI	−3.31071	0.0005	Stable
In first difference
WGI	−6.76179	0.0000	Stable
QL	−5.77758	0.0000	Stable
PP	−7.45773	0.0000	Stable
SI	−7.61580	0.0000	stable
CL	−11.5213	0.0000	Stable
PI	−10.4930	0.0000	Stable

presents the empirical results of the unit root tests, including the Im, Pesaran and Shin tests and the Levin, Lin and Chu tests.

Following this analysis, we can observe discrepancies between the IPS and LLC tests regarding stationarity at the level. The Levin, Lin and Chu test suggests that more variables are stationary at the level compared to the Im, Pesaran and Shin test. In the case of the Levin, Lin and Chu test, all variables become stationary at the first difference, while in the Im, Pesaran and Shin test, only the Purchasing Power Index (PP) remains unstable after the first difference. Since the majority of variables are stationary at the first difference according to both tests, the Autoregressive Distributed Lag (ARDL) model may be appropriate for investigating the long-term and short-term relationships among variables.

After verifying stationarity and confirming that all variables are stationary, we estimate the ARDL model, reflected in

Table 7. ARDL Model.

Variables	Elasticity Coefficient	Standard Error	t-Statistic
QL (−1)	0.631629	0.043933	14.37723
WGI	0.141749	0.035523	3.990342
WGI (−1)	−0.207753	0.034647	−5.996225
PP	0.718730	0.062669	11.46861
PP (−1)	−0.461222	0.080709	−5.714656
PP (−2)	0.178337	0.048540	3.674003
PI	−0.466751	0.041918	−11.13474
PI (−1)	0.372610	0.044123	8.444808
CL	−0.480704	0.063567	−7.562201
CL (−1)	0.361244	0.064773	5.577047
SI	0.077936	0.098777	0.789010
SI (−1)	0.142658	0.098777	1.444236

, to examine the long-term and short-term relationships. The model’s information criteria results suggest the ARDL specification (1, 1, 2, 1, 1, 1). This specification refers to a model analysing the relationship between time series, specifically the number of autoregressive terms included in the model. Thus, in the context of our research, this notation indicates that the ARDL model consists of the following: 1 lag for the dependent variable QL, 1 lag for WGI, 2 lags for PP, 1 lag for PI, 1 lag for CL, and 1 lag for SI.

The ARDL model indicates that the World Governance Indicator (WGI), Purchasing Power (PP), and Cost of Living (CL) are significant factors influencing Quality of Life (QL). WGI has a significant positive short-term effect on quality of life (coefficient of 0.141749) but a negative long-term effect (coefficient of −0.207753 for WGI (−1)).

Table 8. Long-Term Estimation Results.

Variables	Elasticity Coefficient	Standard Error	t-Statistic	p-Value
WGI	−0.179177	0.057525	−3.114752	0.0021
PP	1.183166	0.131907	8.969695	0.0000
PI	−0.255562	0.083042	−3.077499	0.0023
CL	−0.324291	0.107899	−3.005494	0.0029
SI	0.598837	0.089791	6.669226	0.0000

EC = QL − (−0.1792 × WGI + 1.1832 × PP − 0.2556 × PI − 0.3243 × CL + 0.5988 × SI).

highlights the long-term estimation results for the ARDL analysis, which is used to understand the long-term relationships between the analysed variables.

The results show that the Governance Index (WGI) has a negative coefficient of −0.179177, suggesting that an improvement in governance may lead to a decrease in quality of life (QL), possibly due to negative aspects associated with it. This negative coefficient of WGI concerning quality of life may indicate challenges encountered during the implementation of governance reforms. These reforms could have included austerity measures and fiscal adjustments necessary for stabilising the economy, which, in the short term, negatively affected the quality of life. Moreover, existing research suggests that the quality of public administration and institutional performance play a crucial role in shaping the lived experiences of citizens. Therefore, Central, Eastern, and Southern European countries, in particular, have tended to score lower on measures of good governance, including factors like professionalism, press freedom, and the use of information and communication technologies. This poor governance has been linked to lower quality of life outcomes, such as diminished access to public services, reduced trust in institutions, and increased inequality. The findings highlight the complex relationship between governance and real economic outcomes. While enhanced governance quality can promote economic growth [31], the impact may not always translate directly to improved quality of life. This suggests that the benefits of good governance may be tempered by other socioeconomic factors [32].

Additionally, the administrative and institutional transitions and restructurings necessary for improving governance could have generated instability and uncertainty, influencing the perception of quality of life during this period [33,34]. However, the WGI coefficient is statistically significant, with a t-statistic of −3.114752 and a p-value of 0.0021.

The long-term analysis underscores the importance of economic and environmental factors in determining quality of life. The results indicate that higher purchasing power and a higher level of safety have a significantly positive impact on quality of life. In contrast, improved governance, higher pollution levels, and increased living costs negatively affect the quality of life [2,35]. The findings suggest that policies aimed at enhancing economic development and environmental protection can improve citizens’ overall quality of life. Specifically, increased public health spending and investments in sustainable infrastructure can boost economic and environmental outcomes, leading to better living conditions and well-being [36,37]. To improve quality of life in the long term, policies should focus on increasing purchasing power and safety while actively addressing issues related to pollution and living costs. Developing countries should shift their focus from purely quantitative growth to a more balanced approach, prioritising economic and environmental sustainability [38].

Table 9. Short-Term Estimation Results.

Variables	Elasticity Coefficient	Standard Error	t-Statistic	p-Value
QL (−1)	−0.368371	0.043933	−8.384933	0.0000
WGI (−1)	−0.066004	0.020054	−3.291377	0.0011
PP (−1)	0.435845	0.055212	7.894050	0.0000
PI (−1)	−0.094142	0.031058	−3.031167	0.0027
CL (−1)	−0.119459	0.040104	−2.978742	0.0032
SI (−1)	0.220594	0.046428	4.751284	0.0000
ΔWGI	0.141749	0.035523	3.990342	0.0001
ΔPP	0.718730	0.062669	11.46861	0.0000
ΔPP (−1)	−0.178337	0.048540	−3.674003	0.0003
ΔPI	−0.466751	0.041918	−11.13474	0.0000
ΔCL	−0.480704	0.063567	−7.562201	0.0000
ΔSI	0.077936	0.098777	0.789010	0.4309

displays the short-term estimation results for the ARDL analysis, highlighting the dynamic relationships between the analysed variables and quality of life (QL).

The variables with differences (Δ) reflect the short-term changes of these factors. ΔPP has a coefficient of 0.718730, suggesting that an increase in purchasing power has an immediate and significant positive impact on quality of life. In contrast, ΔPI and ΔCL have significant negative coefficients, indicating that an increase in pollution and living costs negatively affects the quality of life in the short term. ΔSI has a positive but statistically insignificant coefficient, suggesting that short-term safety changes do not significantly impact the quality of life in this context. These results emphasise the importance of economic and environmental factors in the short term, suggesting that improvements in purchasing power can have rapid positive effects. In contrast, increased pollution and living costs immediately negatively affect the quality of life. In the long run, the results show that purchasing power has a significant positive effect, while pollution and living costs significantly negatively affect quality of life. These findings highlight the critical role of economic and environmental factors in determining the long-term quality of life outcomes; Refs. [36,39] attest to these results. Moreover, these results also corroborate the theoretical justification.

In summary, the Quality of Life Index is a multidimensional measure that reflects the overall well-being and living standards of a country’s population. The top-ranked countries (Denmark, The Netherlands, Finland, Germany, Austria) demonstrate a comprehensive system of interconnected factors, including healthcare, education, social protection, economic stability, infrastructure, and environmental quality, that contribute to a high quality of life. The low-ranked countries (Romania, Bulgaria, Greece), on the other hand, face challenges in these areas, leading to a lower quality of life for their citizens [40,41,42].

The F-BOND test examines long-term relationships (cointegration) between variables in an ARDL model.

Table 10. Co-integration Bound.

Test Statistic	Value	Signif.	I(0)	I(1)
Asymptotic: n = 1000
F-statistic	14.52445	10%	1.81	2.93
k	5	5%	2.14	3.34
		2.5%	2.44	3.71
		1%	2.82	4.21

’s interpretation is based on comparing the calculated F and t statistics with the critical values for different significance levels.

Since the calculated F value (14.52445) is significantly higher than the critical values, we can reject the null hypothesis of “no long-term relationship” and conclude that there is a cointegration relationship between the analysed variables. This suggests that the selected variables move together in the long term, thereby confirming the validity of the ARDL model in capturing these long-term relationships.

The F-BOND and t-Bounds tests thus demonstrate that the variables included in the model are cointegrated, implying that public policies and other socioeconomic factors analysed have a consistent and sustainable effect on citizens’ well-being over time.

Figure 8 illustrates the “CUSUM” (Cumulative Sum of Recursive Residuals) plot, which is used to check the stability of the parameters of an econometric model over the sample period. In this plot, the blue line represents the cumulative sum of the recursive residuals, while the dotted lines represent the 5% significance confidence intervals. We observe that the “CUSUM” line remains within these significance limits throughout the entire analysed period, indicating that the model is stable and there are no significant structural changes in its parameters during the study period.

Figure 9 highlights the “CUSUM of Squares” plot, which is used to test the stability of the error variance in an econometric model, serving as an extension of the “CUSUM” test. In this plot, the blue line represents the cumulative sum of squared successive deviations, while the dotted lines indicate the 5% significance limits. In this case, the CUSUM of Squares line also remains within the significance limits, suggesting that the variance of the errors is stable throughout the analysed period, and there is no evidence of significant variations in the variance. Thus, the results of both CUSUM tests confirm the stability of the model’s parameters and error variance, reinforcing its validity in analysing the effects of public governance on citizens’ well-being.

5. Conclusions

Our study centred on the relationship between public governance and citizen well-being, emphasising how various policies and governmental measures can influence the quality of life. This theme is particularly crucial in the current context, characterised by significant sociopolitical changes and an increased interest in administrative transparency and efficiency. The results are supported by the conclusions of other research, which show that public governance significantly impacts societal well-being, similar to the findings presented by [1,22]. To achieve this objective, various qualitative and quantitative analysis methods were implemented. Complex methodologies examined public governance’s evolution, dimensions, impact, and implications on citizen well-being.

Our analysis highlighted the importance of efficient governance in enhancing citizen well-being. The collected data demonstrated that effective governance measures, such as governmental accountability, administrative efficiency, and corruption control, significantly improve citizens’ quality of life. Moreover, the study revealed that the European Union member states play a pivotal role in this domain, reflecting a shared concern for improving social well-being through effective public policies.

The applied research methodology included a bibliometric study necessary to extract key terms, scientific co-authorship, and the countries where the studies were published. The empirical analysis initially involved mapping the data for the indicators selected for the research topic (the six governance quality indicators and the composite life quality indicator). Finally, ARDL methodology was applied, an econometric analysis involving an autoregressive model with distributed lags for the period 2012–2022, using a sample of EU-23 member countries.

Regarding the bibliometric analysis, three essential points were considered. The first highlighted the most important keywords used by authors who wrote on our topic. This analysis revealed that, in addition to the keywords we thought, namely “Public Governance” and “Well-Being,” the VOSviewer network highlighted other keywords thematically related to our topic: democracy, challenges, health, public services, green infrastructure, biodiversity, etc. The second point was the analysis of scientific co-authorship, which provided data on the countries where the most articles corresponding to our topic were published: the USA, Germany, the Netherlands, Australia, and England (most of the resulting countries are EU members). The third objective was scientific co-authorship concerning the main authors and the number of citations.

ARDL models revealed significant relationships between public governance variables and citizens’ quality of life in EU-23 member countries during 2012–2022. The results demonstrated that the World Governance Indicators (WGI) have a mixed impact on the quality of life. In the short term, the effect is positive if there is an increase in the level of public governance indicators. In contrast, in the long term, the impact of public governance on the quality of life is negative, possibly due to the challenges associated with implementing reforms. Purchasing Power (PP) and Safety Index (SI) significantly positively affected the quality of life, highlighting their importance in public policies. In contrast, the Cost of Living (CL) and Pollution Level (PI) had significant adverse effects, underscoring the need for measures to reduce these factors to improve citizen well-being.

Long-term and short-term analyses showed that improving governance, purchasing power, and safety contributes to a better quality of life while increasing the cost of living, and pollution reduces it. These findings provide a solid framework for informed public policy decisions to maximise the quality of life.

The implications of our findings are multifaceted and pertinent to public policy formulation. Firstly, transparency and accountability in governance are essential for fostering citizens’ trust in public institutions. Secondly, adequate allocation of resources in critical sectors such as health, safety, and purchasing power can directly impact citizens’ well-being. Thirdly, adopting strategies that promote innovation and sustainable development is crucial for transforming economic potential into sustainable growth. These implications underscore policymakers’ need to prioritise governance reforms that enhance administrative transparency, resource efficiency, and anti-corruption measures.

The primary limitation encountered during our research was the unavailability of data for all EU member states, specifically Cyprus, Latvia, Luxembourg, and Malta. Among these limitations was also the absence of data for some countries regarding the “Climate Index” subdivision of the Quality of Life Index, which led to its exclusion from the empirical analysis. In this regard, it is recommended that future studies on public governance challenges related to maximising citizen well-being consider including more relevant indicators and attempt to include all EU-27 member states.

For future research, it is recommended that the study be extended to incorporate additional variables related to well-being indicators, such as technological advancements and innovations in governance. Comparative studies between EU member states and other global regions over an extended period will enable a more thorough examination of the statistical link between governance and well-being.

In this context, achieving progress in the quality of life requires that governance objectives and visions be oriented towards well-defined and structured frameworks, which are considered essential for promoting good governance and are aligned with the understanding of the broader context in which they arise, within flexible and coherent public policies across the EU, combining the principles of a healthy society with human rights. The key steps to improving the quality of life for the population involve enhancing the functionality and quality of various aspects related to public governance, such as voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption. These improvements in public governance can positively influence funding levels, awareness-raising initiatives, and the overall well-being of citizens. Therefore, strategies to improve public governance may encompass implementing more streamlined methodologies, prioritizing the development of staff skills and service delivery, promoting meritocratic principles, and fostering decision-making transparency. In contrast, specific measures to bolster societal well-being are constrained to a regulated framework that safeguards citizens’ interests within the context of sustainable development.

The study’s preliminary findings indicate the need to enhance and fortify public governance in certain regions of the European Union. This is crucial to develop strategies aimed at improving citizen well-being and mitigating the substantial disparities among EU member states. For countries with relatively higher governance standards, it is essential to maintain their current levels and continue to make further improvements. In contrast, countries with comparatively weaker governance performance should prioritize implementing more efficient strategies, decisions, norms, and regulations, modeled on the practices of countries that have demonstrated quality public governance.

Further research could explore the long-term impacts of specific policies, such as those related to healthcare and education, on quality-of-life indicators within these nations.

European countries need to improve and strengthen resource-allocation methods for public governance. WGI indicators, such as Voice and Accountability, Government Effectiveness, and Control of Corruption, emphasise the importance of transparent and efficient administration. Investments in these areas can lead to more responsible governance and increased citizen trust in public institutions.

Promoting policies that support transparency, accountability, and citizen participation in governance processes is also recommended. Subdivisions of the Quality of Life Index, such as Health, Safety, and Purchasing Power, indicate that investments in these sectors are essential for improving citizen well-being. Proper allocation of funds in these areas can have a direct positive impact on the quality of life.

Additionally, the role of international cooperation and learning from best practices among countries cannot be overstated. Collaborative efforts to enhance governance frameworks and share successful strategies can lead to collective improvements in well-being across different regions. Policymakers must remain committed to continuous improvement and adaptation of governance practices to meet the evolving needs of their citizens. By fostering a culture of accountability, transparency, and innovation, governments can create an environment where citizens thrive, thereby achieving sustainable and inclusive development for all. This, in turn, can lead to the achievement of sustainable and inclusive development that benefits all members of society, regardless of their background or socioeconomic status. Governments must remain committed to continuous improvement and adaptation of their governance practices to meet the evolving needs and expectations of their citizens, ensuring that public policies and programs are responsive, equitable, and aligned with the principles of good governance.

Moreover, strategies that favour innovation and sustainable development must be adopted. This includes improving technological infrastructure and implementing policies supporting research and development. This will transform economic potential into sustainable growth and create an environment conducive to continuous development. Implementing these policy guidelines will improve governance and ensure sustainable growth in the quality of life for citizens.