Exploring Sustainable Development and the SDGs 3, 4, and 8 Using the Carbon Intensity of Human Well-Being and Longitudinal Multilevel Modeling

Abstract

Multilevel modeling statistics for longitudinal examination to explore the connections, relatedness, and interdependency of SDG3 “Good Health and Well-being”, SDG4 “Quality Education”, and economic growth SDGs 8 “Decent Work and Economic Growth” at a country level over the period 2000–2020. This is a novel approach that incorporates “nested” or “hierarchically nested” observations. Health and well-being, as measured in this study by the carbon intensity of human well-being (CIWB), was positively linked to economic growth and is a production function of sustainability. Results indicate that countries investing in the promotion of health and well-being outcomes will increase their economic growth over the long term. In contrast to previous studies, the relationship between education and economic growth was insignificant with the caveat of the indicators chosen. Using the advanced statistical methods adopted here could offer policymakers new insights and tools to focus their efforts to accelerate the progress of sustainable development and achieve the SDG targets by 2030.

1. Introduction

In 2015, the United Nations (UN) set forth 17 Sustainable Development Goals (SDGs) comprising 169 underlying targets to be accomplished by the year 2030, which were agreed by 193 nations and nation-states [1]. These goals, forged through collaborative efforts involving stakeholders and policymakers, aim to foster equality, eradicate poverty, safeguard human rights and the environment, and sustain economic growth to create a world that leaves no one behind [1]. While the UN Agenda 2030 categorizes these goals across three dimensions, namely people, planet, and prosperity, other categories have been added. For example, segmented the goals into four dimensions (society, economy, environment, and means of implementation) [2], proposed six dimensions (dignity, people, planet, partnership, justice, and prosperity) [3], and added health and well-being framed as a quadruple bottom line [4]. Sustainable development focused on people is at the heart of several of the goals. For example, SDG3 aims to promote “Good Health and Well-being”, SDG4 is concerned with “Quality Education” that is inclusive and equitable, SDG5 “Gender Equality” and SDG10 “Reduced Inequalities” tackle equity, and SDG8 endeavors to promote “Decent Work and Economic Growth” for all [1,5]. Here, the connections, relatedness, and interdependency of SDG3, SDG4, and SDG8 are explored given their foundational role in achieving Agenda 2030 [6].

Previous studies demonstrate the critical importance of health and well-being and education in enabling economic growth at both the macro and micro levels [7,8,9,10,11,12,13,14]. Education positively correlates with labor productivity and efficiency, enhancing health outcomes and economic growth [15,16]. Educated individuals are more likely to prioritize disease prevention, contribute to the labor market by maintaining their health, and engage in lifelong learning [11,12,17]. Additionally, governments’ effective management of public health and educational institutions is crucial for fostering sustainability policies and building more resilient nations [9,10]. Education and health play pivotal roles in fostering social cohesion and stability by advancing equal opportunities, diminishing inequalities, and nurturing a sense of community, belonging, and well-being [7,14,18]. Furthermore, assessing the interconnectedness of education, health and well-being, and economic growth is especially important given increasing concerns about the lack of progress towards the SDGs [3].

The present study used multilevel modeling statistics (MLM) for a longitudinal (repeated measures) examination of the relationships among SDG3, SDG4, and SDG8 indicators using country-level World Bank data spanning the years 2000 through 2020 inclusive. The research analyzed the global measurement trends of these three SDGs and examined their empirical relationship. Additionally, the carbon intensity of human well-being (CIWB) measurement, a sustainability production function that considers the ratio of carbon emissions to life expectancy [16,19,20,21,22], was included.

Discussions on this topic often focus on the relationship between CO₂ emissions and economic development [23,24,25]. However, recent research approached carbon emissions from the perspective of human well-being, which can be summarized into two main research paradigms. Firstly, the Human Development Index (HDI) has been modified to include CO₂ emissions as an indicator [25]. De La Vega et al. integrated the CO₂ per capita metric into the HDI to create a carbon-sensitive Human Development Index (HPDI), which assessed the levels of human development in 165 nations from 1993 to 1998 [26]. Secondly, sustainable development can be measured by examining environmental performance and human well-being. Reducing CO₂ emissions will ultimately benefit regional and national well-being and positively impact long-term sustainable economic development. The concept of CIWB offers a new perspective for analyzing this issue [25]. Therefore, similar to the abovementioned research and this new insight, this research measures well-being as a function of the CIWB.

Many studies measure the relationship between economic growth, health and well-being, and education. However, to our knowledge, they use different statistical measurements and do not use the CIWB for well-being measurements. Therefore, to address this gap, our research, to our knowledge, is the first to add the CIWB and uses the unique MLM statistics approach on an extensive 20-year dataset across 192 countries.

This approach can offer policymakers and stakeholders a tool to measure the achievements of the SDGs. The unique MLM for a longitudinal (repeated measures) approach that incorporates “nested” or “hierarchically nested” observations underscores the innovation of this study. Additionally, this method is capable of managing missing data effectively [27]. This approach can be used to help identify policies and programs that strengthen social foundations, promote a more cohesive society, and provide valuable evidence for monitoring indicators related to education, health and well-being, and economic growth. It can help countries gauge their progress and identify areas requiring further attention and investment. Overall, continuing rigorous empirical research on the social development formation of systems related to these global goals can help deepen the understanding of the complex interactions necessary for effective policy making and the pursuit of sustainable long-term growth.

2. Literature Review

Previous studies of economic growth highlight the solid and positive relationships among the three SDGs explored here, namely SDG3, SDG4, and SDG8 [7,13,28,29,30,31]. The first subsection outlines contextual literature on sustainability, the second considers education and economic growth, and the last section focuses on the relationships between health and well-being and economic growth.

2.1. Sustainability

When the UN introduced 17 SDGs and underlying 169 targets in 2015, they set a timeline for them to be accomplished by 2030—indeed, many refer to this as Agenda 20,230 [1]. These goals encompass a wide range of objectives, including eradicating poverty, ensuring zero hunger, promoting good health and well-being, providing quality education, achieving gender equality, ensuring clean water and sanitation, and fostering sustainable economic growth [32]. They aim to address various facets of economic growth, social development, environmental protection, and the alleviation of hunger and poverty [1]. Furthermore, given their global reach, sustainable development necessitates international and transnational co-operation and research [33]. A comprehensive understanding of the SDGs reveals their interconnectedness, with each goal contributing to, complementing, and/or enabling many of the others [14]. For instance, education features prominently across multiple SDG targets, including those related to health and well-being (SDG3), gender equality (SDG5), and decent work and economic growth (SDG8). Furthermore, the targets associated with education extend beyond traditional academic outcomes to encompass broader objectives such as global citizenship, sustainability, and gender equality [34]. Individuals with higher levels of education tend to secure better employment opportunities, experience improved health and well-being, actively engage in their communities, advocate for equality, and participate more actively in civic life [15,17,35,36]. Since 2015, sustainable development has emerged as a pivotal concept with growing importance across policies, communities, businesses, and nations worldwide. It serves as a cross-cutting issue spanning various disciplines, particularly within economics [5]. Additionally, sustainability has made significant research contributions and has garnered attention in international literature and politics [37,38,39,40,41,42,43,44,45].

2.2. Education and Economic Growth

The correlation between education and economic growth has been extensively documented [31,46,47,48,49,50]. A well-educated workforce catalyzes both decent work opportunities and robust economic expansion. Higher levels of education yield skilled labor and workers who work with greater efficiency and effectiveness, thereby directly or indirectly stimulating economic growth. For instance, Bunello and Comi analyzed the impact of education on economic growth across 11 European countries [28]. Their empirical findings highlight the significant influence of tertiary and secondary education on earning profits and its sustainable advantage in the labor market. To clarify, the annual earnings growth for college graduates with labor market experience in Portugal, which has the highest productivity growth in the sample, is approximately 5.25% higher than the earnings growth of college graduates in the Netherlands, where the earnings growth rate is the lowest. This differential in earnings growth decreases to 3.43% per year for high school graduates and to 1.61% for employees with less than upper secondary education [28]. Bunello and Comi argued that skilled workers demonstrate higher productivity than their less skilled counterparts, thereby contributing to economic growth [51]. Similarly, Sarwar et al. identified a significant correlation between education and economic growth in low-income and developing countries [13]. Chansarn conducted an empirical study on the labor productivity growth rates of 30 countries during the period 1981 to 2005 and showed that all countries experienced growth rates exceeding 4% over the period [7]. Bhorat et al. observed a notable increase in per capita income associated with higher levels of education and emphasized education’s substantial and positive impact on economic growth across different demographic groups, asserting that global economies should raise education levels to drive economic progress [48]. Ahsan and Haque conducted an empirical analysis across 126 countries, examining the relationship between years of schooling and economic growth from 1970 to 2012 and suggested a significant positive effect of schooling on economic growth [47].

2.3. Health and Well-Being and Economic Growth

There exists a significant correlation between various health and well-being indicators (such as mortality rates, morbidity, infant mortality, and tuberculosis incidence) and economic growth [6,13,52,53,54,55]. Studies demonstrate that health issues negatively influence economic growth across different countries, with individuals in affluent nations enjoying longer life expectancies compared to those in poorer countries. Furthermore, conventional economic growth metrics, such as gross domestic product (GDP), must capture crucial aspects of well-being, such as health, social connections, and environmental quality.

Afzal et al. [46] demonstrated that increased physical activity in Asian countries correlates with reduced health issues, contributing to enhanced labor productivity and economic growth. Similarly, McMichael argued that lower rates of health problems facilitate sustainable economic growth [56]. Furthermore, Greer and Kuhlmann, in a study on the impact of European Union policies on education and health, assert that broader asymmetries and political dynamics are mediated through health and influence economic growth [57]. Ogundari and Awokuse used a dynamic model based on the system generalized method of moments (SGMM) to analyze panel data from 35 countries spanning 1980 to 2008 [30]. Their findings revealed that health significantly impacts economic growth, with a 10% increase in life expectancy at birth corresponding to increases of 4.9%, 2.2%, and 2.6% per capita [30]. Deaton and Schreyer noted that individuals in wealthy countries typically have an average life expectancy of nearly 80 years across the OECD region [53]. Moreover, affluent nations typically boast superior healthcare systems, possess a greater capacity to substitute electronic communication for physical contact, and maintain higher levels of wealth resilience during income downturns.

Pursuing a dynamic equilibrium between economic advancement and human welfare is a pivotal objective within the sustainable development framework. This stems from recognizing that economic progress can help to enhance human well-being [16,58] and vice versa. Sustainability is often measured using a widely recognized metric focusing on the relationship between the goods nations produce and their environmental impact. This metric evaluates “environmental efficiency in producing human well-being” (EWEB). In this context, sustainability is defined as the efficiency with which natural resources are utilized to enhance human well-being [25]. Jorgenson first introduced the Consumption-based Index of Well-being (CIWB) while analyzing the interplay between economic growth and the CIWB across regional samples of nations from 1970 to 2009 [20]. Recent research has delved into carbon emissions through the lens of human welfare, often referred to as the carbon intensity of human well-being (CIWB). Being a production function of sustainability, CIWB considers the ratio of carbon emissions and life expectancy [19,20,21,25,59,60].

The examination of the influence of economic development on CIWB aligns with the foundational principles of sustainable development [19,20]. For instance, Dietz et al. analyzed panel data from 58 nations and uncovered a U-shaped relationship between GDP per capita and the environmental intensity of human well-being, contrary to the traditional Kuznets curve [19]. Jorgenson and Givens investigated changes in the effect of economic development on consumption-based CIWB across 69 nations from 1990 to 2008 and observed an increasing influence of economic development on CIWB over time for the entire sample [21]. However, in the early 1990s, heightened development reduced CIWB for non-OECD nations in Africa, though this relationship shifted towards less sustainability in recent years between CIWB and economic development [21]. Sweidan explored the impact of economic performance variables on CIWB across 13 countries in the Middle East and North Africa (MENA) region from 1995 to 2013 and showed a statistically significant positive influence of economic performance on CIWB during this period. This suggests that, while economic performance initially harms the environment, the overall effect stabilizes over time [61]. Zhang et al. used panel data spanning 114 nations from 1980 to 2014 to construct an index measuring the CIWB and reported that both economic growth and mortality rates contributed to increases in CIWB [16]. Notably, the positive impact of economic growth on the CIWB was most pronounced in developing countries in Latin America, followed by Asian developing and developed nations, with the most negligible impact observed in African developing countries [16].

3. Methodology

3.1. Research Questions

The research questions explored in the present study relate to the relationship among three SDGs (3, 4, and 8) and economic growth. Specifically:

• RQ1: To what extent are the SDGs, good health and well-being and education, at a country level, associated with the economic growth SDGs for the years 2000–2020? The research variables are presented in

  Table 1. Study research variables.

  	Variables	Variable Values	Measurement Scale	Type of Variable
  Interval-Rational	GDP growth (annual %)	Economic growth	Dependent variable	Quantitative
  Interval-Rational	School enrollment, secondary (% gross)	Education	Independent variables	Quantitative
  Interval-Rational	Incidence of tuberculosis (per 100,000 people)	Health		Quantitative
  	Well-being	${\displaystyle \frac{{\mathrm{C}\mathrm{O}}_2 \mathrm{e}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}\left(\mathrm{m}.\mathrm{t} \mathrm{p}\mathrm{e}\mathrm{r} \mathrm{c}\mathrm{a}\mathrm{p}\mathrm{i}\mathrm{t}\mathrm{a}\right)+24.2}{\mathrm{L}\mathrm{i}\mathrm{f}\mathrm{e} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{y} \mathrm{a}\mathrm{t} \mathrm{b}\mathrm{i}\mathrm{r}\mathrm{t}\mathrm{h}\left(\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{s}\right)}}$ *	Interval-Rational	Quantitative

  * The equation for CIWB derived from Kelly (2020) [60].

  .

3.2. Data and MLM Model

The data in this study were generated from the World Bank website for 2000 to 2020 at a country level (n = 192) and cover 20 years (T = 20). To build data with country-level only, missing system and Word Bank special coding, for example, HIPC, IBRD, IDA, etc., were excluded from the data. The MLM for longitudinal (repeated measures) statistics approach was used to answer the research questions. Multilevel analyses (MLM) have become increasingly common in research, primarily due to their capacity to manage missing data, particularly when data are unavailable for certain countries and years [27]. Multilevel data structures contain observations that are grouped within a hierarchy. The term “group” refers to the data’s clustering unit or nesting structure. This makes sense when studying defined groups like schools, work teams, countries, etc. However, the “group” label can be confusing when data are nested in other ways, like repeated observations within individuals over time. In these cases, the individual constitutes the “group”. Likewise, in a cross-cultural study, cultures essentially function as groups, with individuals nested within cultures [62,63,64]. The critical characteristic of multilevel data is that the observations are not independent. There is an interdependence among the nested observations, such that units from the same group are more similar than units from a different group. This hierarchical nature of the data needs to be considered in the analysis. Failing to account for the nonindependence of nested observations can lead to inaccurate findings.

Multilevel statistical models have been developed to analyze hierarchically structured data properly and correct for the shared variance within clusters. Carefully examining and modeling such data allows for theories to be tested and valid conclusions to be drawn [27,65,66,67]. In the current study, World Bank data were used to measure the global trend and the relationships among SDGs 3, 4, and 8 for the years 2000 to 2020. The observations in each county were nested together. These variables (education, health and well-being, and economic growth) were measured the same way for each country every year for the last 20 years (repeated measures). Therefore, it is important to consider the role of time in understanding developmental processes. When we measure an outcome multiple times for an individual or country, it is called a repeated measure design (RMD). For example, a simple RMD is the pretest and post-test design or measurement every year. When studying developmental processes, including measurement occasions between the pretest and post-test can offer a more comprehensive examination and often increase the statistical power to determine if a change has occurred [65]. Therefore, MLM for a longitudinal (repeated measures) approach is the best statistical approach to answer our research questions.

When specifying a repeated measures analysis using a mixed-model approach, several considerations must be kept in mind. First, we need to decide whether we are examining one or more growth processes. Second, after deciding on an approach to use, we need to consider the expected within-subjects (level 1) effect for time. This involves examining individuals’ potential to change over the study period. The time effect helps us understand if individuals are changing over time and by how much [65]. The level 1 part of the model represents the anticipated change that each member of the population is expected to experience during the study period (2000–2020). We assume that the observed status for a country i at time t is a result of the growth trajectory and a random effect. At level 2, differences in trajectories between subsets of countries (e.g., education and health and well-being) are considered. The general Model-1 and Model-2 equations are as follows:

$${\mathrm{E}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}}_{\mathrm{i}\mathrm{j}}={\mathsf{\pi }}_{\mathrm{o}\mathrm{i}+}{\mathsf{\pi }}_{1\mathrm{i}}\mathrm{T}\mathrm{i}\mathrm{m}\mathrm{e}$$

(1)

$$\begin{array}{l}{\mathrm{E}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}}_{\mathrm{i}\mathrm{j}}\\ ={\mathsf{\pi }}_{\mathrm{o}\mathrm{i}+}{\mathsf{\pi }}_{1\mathrm{i}}\mathrm{T}\mathrm{i}\mathrm{m}\mathrm{e}+{\mathsf{\pi }}_{2\mathrm{i}}\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}+{\mathsf{\pi }}_{3\mathrm{i}}\mathrm{H}\mathrm{e}\mathrm{a}\mathrm{l}\mathrm{t}\mathrm{h}+{\mathsf{\pi }}_{4\mathrm{i}}\mathrm{W}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{b}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{g}+{\mathsf{\pi }}_{5\mathrm{i}}\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\times \mathrm{T}\mathrm{i}\mathrm{m}\mathrm{e}\\ +{\mathsf{\pi }}_{6\mathrm{i}}\mathrm{H}\mathrm{e}\mathrm{a}\mathrm{l}\mathrm{t}\mathrm{h}\times \mathrm{T}\mathrm{i}\mathrm{m}\mathrm{e}+{\mathsf{\pi }}_{7\mathrm{i}}\mathrm{W}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{b}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{g}\times \mathrm{T}\mathrm{i}\mathrm{m}\mathrm{e}{Ɛ}_{it}\end{array}$$

(2)

We believe that the MLM approach is superior to other methods for several reasons. First, our sample was hierarchical and nested within a country. As a result, traditional regression methods, which assume independence between observations and rely on a single fixed intercept and slope, are less reliable. This can lead to inaccuracies in standard errors. Second, the data we utilized from the World Bank contained missing values and included groups of different sizes. Unlike other methods, such as ANOVA, which struggle with unequal group sizes and missing values, MLM is robust to these imbalances. Third, MLM allows for random intercepts and slopes. In contrast, fixed-effect models assume only fixed effects and treat groups as categorical variables. Lastly, MLM is better suited for longitudinal data with varying time points compared to ANOVA.

3.3. The Sample

The sample comprised 192 countries. On average, the GDP growth (annual %) was 3.19, with a standard deviation (SD) of 5.5. The average secondary school enrollment (% gross) stood at 79.3, with a standard deviation of 29.5. The average incidence of tuberculosis (per 100,000 people) was 141.26, with a standard deviation of 198.17. The average life expectancy at birth (total years) was 69.48, with a standard deviation of 8.91. Additionally, the average CO₂ emissions (metric tons per capita) were 4.19, with a standard deviation of 5.36.

4. Results

The present study used a country-level setting for the years 2000 to 2020 and an MLM for a longitudinal (repeated measures) approach applied using SPSS (IBM SPSS statistics version 30). To examine the hypothesis that health, well-being, and education enhance economic growth, a multilevel growth curve approach using SPSS was applied.

First, a null model was run in which the variation in the dependent variable, economic growth, was partitioned into its between country and within the country (see

Table 2. MLM results.

	Null Model	Within Country Level	Moderation Effects
Fixed effect
Model 1:
Intercept	3.4 **	5.22 **	4.91 **
	(0.14)	(0.197)	(0.23)
Time		−0.181 **	−0.127 **
		(0.015)	(0.16)
Model 2:
Well-being			9.12 **
			(3.05)
Health			−0.001
			(0.00)
Education			−0.007
			(0.007)
Well-being × Time			−0.43
			(0.246)
Health × Time			0.03 **
			(0.00009)
Education × Time			−0.0008
			(0.0005)
Variance components
With country	27.7	25.9	19.3
Between country	2.7	2.7	3.48
Time trajectory		0.001	0.001
Proportion explained
within		0.06
Time trajectory			0.45

** p < 0.001.

). Within country fluctuations in economic growth (${\sigma }^2=27.7)$ accounted for 6% of the overall variance and between country variability ($\tau =2.7$) accounted for the remainder (ICC).

To ease coefficient interpretation, time was centered on the first year, health and well-being and education were centered to the grand mean within country intercepts, and slopes were treated as random effects (i.e., a random intercept and slope model). Table 2 presents the unstandardized MLM coefficients. First, we ran a within-country model in which the association between time and economic growth was estimated and results show a significant negative time effect (β = −0.181, S.E = 0.015 p < 0.001), suggesting that economic growth decreases with time. Lastly, we estimated the extent to which health and well-being and education moderated the trajectory over time. The results demonstrate that health and well-being is positively associated with economic growth (β = 9.12, S.E = 3.05 p < 0.001). Moreover, as presented in the moderation model (Table 2), a significant interaction was observed. Results indicated that countries that invest in promoting health and well-being outcomes will increase their economic growth over the long term (β = 0.00, S.E = 0.00009 p < 0.001).

5. Discussion

The present study examined the relationships between three SDGs, namely good health and well-being (SDG3), quality education (SDG4), and decent work and economic growth (SDG8) indicators. The research analyzed the global trends in measurements of these three SDGs and investigated their empirical relationship using a unique MLM longitudinal (repeated measures) approach.

The study indicates that economic growth decreased from 2000 to 2020. Additionally, health and well-being, as measured in this study by CO2 emissions and life expectancy (carbon intensity of human well-being (CIWB) measurement), is positively linked to economic growth. Moreover, this research suggests that countries investing in health will likely experience increased economic growth in the long term.

A significant correlation between various health and well-being indicators (such as mortality rates, morbidity, infant mortality, and tuberculosis incidence) and economic growth [6,13,52,53,54,55] has been described previously. The results in this study align with the literature and used a novel approach of MLM with CIWB.

However, in contrast to previous studies, we observed that the relationship between education and economic growth was insignificant. This may be due to the differences in how the education variable is measured in this study (i.e., school enrollment, secondary (% gross) versus other studies (years of schooling est.).

In further research, we recommend extending the equation and adding other SDGs, such as hunger and gender equality, which may also be related to economic growth. In addition, we recommend measuring education in other ways, such as literacy rates, outcomes, and est. In our research, similar to other research in this era, the educational measurement was school enrollment (% gross), chosen for two main reasons: first, this variable can be comparable between 192 countries and, second, the data were accessible. Moreover, we believe it will be interesting to measure a causal effect between them using a causality statistics approach and to divide the data at a country level so each can measure progress separately. We also suggest that future research examines the research variable measured in this study at a country level and indicates specific descriptive statistics. This approach can help identify the countries with the highest or lowest correlations.

6. Conclusions

In conclusion, the current study advances the literature in the fields of sustainability, economic growth, education, and health and well-being. It is innovative in that it examined the well-being variable through CO₂ emissions and life expectancy as a significant factor in assessing economic growth. In other words, our implications are that countries interested in economic growth and achieving the SDGs will need to emphasize the level of CO₂ emissions in their country. This clearly has both political and societal ramifications. Using the advanced statistical methods adopted in the present study to examine the relationships between the research variables and their development over the years can offer policymakers new insights and tools to focus their efforts on making progress towards the SDG targets for delivery by 2030. This approach can offer valuable feedback on the effectiveness of policies, investment priorities, and initiatives implemented to achieve these goals. Additionally, this study’s findings, which indicate a decline in economic growth over the years studied, serve as a warning sign to policymakers to review existing policies. It shows the centrality of investing in health and well-being to improve long-term economic growth.