The Impact of Pollution and Carbon Emission Control on Financial Development, Environmental Quality, and Economic Growth: A Global Analysis

Abstract

The global environment has recently been facing sustainability threats owing to industrial and economic expansions. Accordingly, this study empirically examines the impact of carbon emissions and the directional causality between carbon emissions and environmental quality, financial development, and economic growth. We used data from 65 economies from 2010 to 2021, applying fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) approaches. Generally, the findings from the analysis revealed that the estimated coefficients of carbon emissions were negative and significant across the model, except for greenhouse gas emissions, which produced an insignificant result in developed economies. This result proves that an increase in carbon emissions and other forms of pollution are detrimental to environmental quality, economic growth, and financial development. Further results revealed that fossil fuels are positively and significantly related to the economic growth and financial development of selected countries. Empirical outcomes indicate that ineffective control of environmental pollution and carbon emissions is a major challenge to the economic growth trajectories of the selected countries, especially in emerging economies. The results from directional relationships revealed that bi-directional causality exists between CO₂ and GDP; between total greenhouse gas emissions and economic growth, with no directional relationship of CO₂ emissions to financial development and vice versa; and economic growth to CO₂ emissions from gaseous fuel consumption and vice versa. Generally, this outcome indicates that improved environmental quality control can accelerate economic growth and financial development worldwide. This study provides insights to governments, policymakers, international organizations, researchers, and many other stakeholders. This study suggests that stricter fiscal and monetary policies, laws, and regulations, such as environmental taxes and carbon emission taxes, with strong implementation strategies, especially in emerging economies, are strongly recommended worldwide. Further recommendations suggest the development of technologically innovative policies that can counter all the impacts of devastating human activities on the environment, and these are encouraged. A greater consumption of renewable energy and the use of other innovative machines that are environmentally friendly and can help control various forms of pollution and carbon emissions have been encouraged globally.

1. Introduction

Carbon emissions and other forms of pollution have drawn significant attention recently due to their direct impact on environmental quality and potential threat to future generations [1,2,3,4,5,6,7]. To reduce environmental degradation, many countries have implemented stringent policies and controls aimed at achieving better environmental quality and sustainable development goals [2,3,5,6,8]. In order to achieve these goals, climate scientists have emphasized the need for the regulation and control of human activities, population, industrial, economic activities, and development of all kinds [4,6,9,10,11,12,13,14,15,16,17]. According to the Intergovernmental Panel on Climate Change (IPCC) report five, pollution and carbon emissions are significant threats to ecosystems and environmental quality globally, contributing to increased climate change and challenging the achievement of sustainable goals [13,18]. Economic performance drivers, including industrialization, globalization, economic growth, and financial development, play a crucial role in determining the volume of carbon emissions and influencing global environmental quality and performance [6,16,17,18,19]. Despite the carbon emission reduction of 24% achieved by the European Union in 2019, the [9], in an article by [15], states that global emissions are still quite high.

Throughout history, the global environment has experienced a significant increase in carbon emissions (CO₂ and fossil fuels) since the post-World War II economic boom of the 1940s to the 1980s, primarily originating from industrialized nations [18]. In response, countries have implemented overlapping policies aimed at reducing the environmental damage associated with various activities to ensure a safe and hazard-free environment [20,21,22]. As a result, there has been a growing demand from environmentalists, scholars, policymakers, and other stakeholders to protect the environment from the adverse effects of human activities, such as excessive energy resource use, and to highlight the potential risks associated with these activities [3,9,18,22,23,24]. This demand has led to calls for the control of climate change, carbon emissions, and other forms of pollution, which have been the subject of much debate and research across various disciplines. More recently, the need for Sustainable Development Goals, as advocated by the United Nations, including the goal of addressing climate change (Goal 13) [14], has gained prominence. Moreover, the need for environmental sustainability has increased the importance of pollution and emission control [1,2,16,19,25]. This can be achieved through the implementation of stringent emission and pollution control measures, such as tax policies, penalties (fines), and the transition from non-renewable to renewable energy sources, which can only be achieved under a strong and guaranteed environmental control policy framework, as suggested by various researchers [16,26]. The growing transition to renewable energy consumption for a healthier environment is a pathway towards making the global environment sustainable [16,27]. Sustainable Development Goals include affordable and clean energy (goal 7) and action to reduce the impact of climate change (goal 13) to enhance environmental quality with adequate control [14]. At the country level, financing and maintaining environmental quality should start with yearly budget allocations, governance, and other environmentally related policies that can help improve the quality of the environment [28]. Researchers have revealed that rapid global economic growth, with rising industrialization, globalization, and financial development, has triggered the rate of carbon emissions [16,29]. Ref. [30] averred that part of control could be attributed to the reducing consumption of non-renewable energy such as coal and fossil fuel.

The primary objective of this research is to alert global policymakers to the necessity of adopting a more proactive stance in environmental management for sustainable development. The present rise in environmental degradation and other issues stemming from industrialization, economic growth, and financial development is a source of great concern. This poses threats not only to the present generation but also to the survival and sustainability of future generations, who may be denied their inheritance [8]. In light of this, this study makes a valuable contribution to the existing body of literature on several fronts. Firstly, to the best of the authors’ knowledge, this research is one of the first empirical study to explore the effect of pollution and carbon emission control on financial development and economic growth on a global perspective. The extant studies have predominantly concentrated on either regional- or country-specific analysis with no evidence from a global perspective (see, for instance [6,16,18,31,32,33,34,35]). To achieve this, this study was divided into sub-panels: developed, emerging, and full-sample global views. In this way, this study expands the body of knowledge and contributes to the global literature on the subject. Secondly, this study extends the extant literature by employing multiple econometric strategies, including FMOLS and DOLS, Driscoll–Kraay standard error (DKSE) and Dumitescu-Hurlin causality techniques. This research distinguishes itself from previous studies by utilizing these econometric approaches to provide robust and consistent outcomes, taking into account cross-sectional dependency, endogeneity, and heterogeneity to address methodological deficiencies in the literature. Thirdly, this study stands out as one of the pioneering investigations to link carbon emissions and environmental control to the Sustainable Development Goals (SDGs), an area of research that remains scarce and highly relevant in the global literature. Fourth, the findings of this study will provide valuable implications for policymakers and stakeholders to formulate efficient and sustainable policy strategies for achieving SDGs on a global scale. Lastly, this study clarifies the ongoing debates and conflicting findings in the literature. For instance, while some scholars argue that financial development and economic growth improve environmental quality [6,36,37], others contend that it degrades the environment through carbon emissions and other forms of pollution, causing more environmental damage [4,16,17]. Previous studies have established the relationship between environmental quality and economic growth [17,37,38], carbon emissions and financial development [39], carbon emissions and industrialization [39], carbon emissions and economic growth [33,34,37,40,41], financial development and environmental quality [6,39,42], environmental quality and globalization [7,16,39], and carbon emissions and renewable energy [16,28].

In general, the achievement of environmental control appears to be challenging due to the fact that many countries and companies, especially those in emerging economies, continue to operate below industrial standards, despite the implementation of policies aimed at reducing pollution and emissions, which have had a detrimental impact on the global environment [2,13,18]. As a result, there is an increasing need for innovation and the development of more stringent policies on a global scale [6,16,34,43]. This has been the driving force behind the growing body of research in this area of study. While past empirical evidence has been mixed, conflicting, and controversial, it is attributed to various firm-specific and country-specific factors, including different mixed variables [18,20,32,33,35]. This study addresses all of these issues and adds to the existing literature. To address these problems, this study examines the impact of carbon emissions control on environmental quality, financial development, and economic growth; discusses the relationship between carbon emissions and these factors to reveal the need for emission control; and offers policy recommendations. The remaining sections of this study are organized as follows: concepts, theory, and literature review; methodology; results and discussions; and finally, conclusion and policy recommendations.

2. Concept, Theory, and Literature Review

This section encompasses three parts of this study, beginning with the conceptual literature, followed by a review of the theoretical literature, and lastly a review of the empirical literature.

2.1. Theoretical Perspective

Since the economic boom of the 1970s, nations have been endeavoring to mitigate environmental damages through policy implementation, with the aim of ensuring a safe and hazard-free environment [21]. This struggle has become increasingly prominent and evident due to the current level of growth and development. Carbon emission control, in the context of growing industrialization, globalization, and financial and economic development, has garnered significant attention recently due to its direct impact on the environment [7]. One of the global objectives is to enhance environmental quality by reducing environmental damages through regulations, policies, laws, and activities within the frameworks of the United Nations to achieve environmental sustainability. Environmental sustainability is defined as the determination of the optimal point at which environmental damages would be maximally reduced to achieve sustainable growth and development. Several drivers of economic performance, namely industrialization, economic growth, and financial development, influence the volume of carbon emissions and their impact on environmental quality and performance globally [16,17]. Extant studies in this field have established the relationship between carbon emissions and economic growth, financial development, environmental performance, FDI, and other factors, with most revealing a strong correlation [6,16,18,20,31,32,33,34,35,41]. However, no global study has been conducted to the extent of the findings presented in this study. Recently, the focus has shifted towards the regulation and control of human impacts to enhance environmental quality, as the environment is inextricably linked to all human activities that contribute to economic performance. Consequently, environmental control is imperative to improve environmental quality, mitigate climate change, and achieve other sustainable development goals. This study addresses these and numerous other pertinent issues. Thus, this research is unique, relevant, and timely, considering the recent increasing rates of environmental damage globally that require urgent attention. The Figure 1 below is a presentation of a chart conceptualizing the study framework is essential to enhance the comprehension of this research.

The flowchart above represents the study’s conceptual framework. As part of global initiatives to address environmental issues, the research addressed this matter and has demonstrated strong negative correlations between human activities and environmental quality [6,16,32,34,35,44]. The pursuit of development by nations results in environmental degradation, which could undermine the envisaged global desired quality of the environment [45]. Governmental interventions through stringent policies, laws, and regulations, such as tax policies at the national and global levels, as well as adequate follow-up and punitive measures to ensure compliance, need to be enhanced to achieve the desired environmental control. If this is globally implemented, it would improve the quality of the environment and mitigate the global rates of climate change. This would consequently ensure the quality of life for present and future generations. This study presents a general review of the related literature.

2.2. Empirical Literature

Extant studies on the relationship between environmental quality, financial development, and economic growth have yielded diverse results. Notable among them are [31], who investigated the impact of financial development on CO₂ emissions and found that financial development efficiency and stock trading volume have a positive effect on CO₂ emissions. They further determined that the financial development and market value of listed companies have a negative impact on CO₂ emissions. Similarly, ref. [19] explored the influence of economic and financial development on carbon emissions and reported that financial development mitigates long-term emission rates. Further research by [6] analyzed the nexus between financial development and environmental quality and investigated the technological effect of financial development on environmental quality. Their findings indicate that financial development is a positive driver of environmental quality and is negatively correlated with CO₂ emissions and population increases. Ref. [18] examined the relationship between energy consumption, carbon emissions, economic growth, trade openness, and urbanization using panel cointegration and the Granger causality test. The empirical results demonstrate a significant connection between energy consumption and carbon emissions, GDP, and trade openness. Further results show a short-run unidirectional relationship between energy consumption and trade openness, urbanization and carbon emissions, and from GDP to energy consumption. The study by [46] analyzed the relationship between carbon emissions and economic growth in Algeria using the ARDL approach. Their findings support the Environmental Kuznets Curve (EKC) hypothesis in Algeria, with a notably high GDP per capita threshold. This suggests that economic growth and energy consumption in Algeria will continue to increase until this threshold is reached. In addition, ref. [20] investigated the long-run causal relationship between energy consumption, CO₂ emissions, and economic growth, establishing a bi-directional relationship between economic growth and carbon emissions. Furthermore, ref. [41] explored the nexus between carbon emissions, economic growth, and financial development in GCC countries and found unidirectional causal relationships among CO₂ emissions, financial development, GDP, and energy use in all GCC countries, except for the UAE. Similarly, ref. [33] examined the impact of income, energy consumption, and population growth on CO₂ emissions by employing ARDL, and the results revealed that income and energy consumption increase CO₂ emissions and supported the EKC hypothesis in India, China, and Indonesia. Ref. [44] examined the ecological consequences of CO₂ emissions on economic growth, FDI, and financial development in selected Asian countries by employing DOLS and FMOLS techniques, and the findings reveal that FDI and financial development are statistically significant and have a long-run relationship with CO₂, which suggests its alignment with EKC in the selected countries. Ref. [35] analyzed the nexus between financial development, CO₂ emissions, and economic growth for sub-Saharan African countries using the causality test and pooled mean group PMG and ARDL techniques with dynamic GMM techniques. The empirical results reveal that FD reduced CO₂ emissions in the long run, supporting the EKC hypothesis with a bi-directional causality relationship. Ref. [32] investigated the impact of energy consumption, globalization, financial development, and urbanization on carbon emissions. The results indicate that financial development and energy consumption contribute to emission rates, support the Environmental Kuznets Curve (EKC), and a bi-directional causality relationship exists amongst the variables, except for globalization and urbanization, which revealed a unidirectional relationship with carbon emissions. Ref. [34] examined the dynamic association between financial development, globalization, and carbon emissions in Asian countries using updated, fully modified methods subjected to EKC frameworks. The study found that financial development and globalization reduced CO₂ emissions, whereas economic growth and energy density increased CO₂ emissions. Ref. [16] explored the influence of trade openness, economic growth, financial development, and non-renewable energy utilization on CO₂ emissions in Pakistan. The findings revealed that financial development, non-renewable energy, and trade openness deteriorate environmental quality with a unidirectional causality relationship. Ref. [2] investigated the synergistic emission reduction effects of the emission trading system (EST) on air pollution and carbon emissions. The findings indicate that the implementation of ETS reduces both carbon emissions and air pollution. Ref. [8] examined the causal relationship between globalization, renewable energy consumption, financial development, and CO₂ emissions, as well as its implications for sustainable development, by employing PFMOLS and PVECM. The results demonstrate a unidirectional long-run causality from CO₂ to financial development and globalization in the urban population, with only renewable energy consumption significantly negatively related to CO₂ emissions.

Furthermore, ref. [47] analyzed the dynamic association between financial development, natural resources, globalization, non-renewable and renewable energy consumption of greenhouse gas emissions, and economic growth in Arctic countries. The findings revealed that financial development and renewable energy consumption increase environmental damage, which undermines environmental quality, while globalization, economic growth, and non-renewable energy increase environmental degradation. Additional findings revealed that financial development, natural resources, globalization, and non-renewable and renewable energy boost economic growth. Similarly, ref. [17] analyzed the impact of financial development, FDI, economic growth, electricity consumption, and trade openness on environmental quality using a panel causality approach. The findings revealed that an increase in FDI, trade openness, and financial development improves environmental quality. Further findings revealed that economic growth and electricity consumption degrade environmental quality. A bi-directional causality was found amongst economic growth, FDI, financial development, electricity consumption, and trade openness with environmental quality. In addition, ref. [48] examined the relationship between financial development, economic growth, and environmental quality by employing PVAR in a Generalized Method of Moment (GMM). The results revealed that financial development had a negative impact on carbon emissions. Moreover, ref. [49] examined both the direct and indirect effects of financial development on environmental pollution and EKC using a GMM of data from 88 developing countries. The results indicated that financial development reduces the adverse effects of income, trade openness, and FDI on pollution emissions with an indirect association. Ref. [50] investigated the effect of financial development and economic growth on the ecological footprint by including non-renewable energy consumption and trade openness as additional determinants using the Westerlund and Edgerton panel LM bootstrap. The results revealed that financial development, economic growth, and non-renewable energy consumption negatively affect environmental quality. Furthermore, ref. [45] investigated the dynamic interaction between financial development, economic growth, and globalization with carbon emissions in Vietnam using the quantile-on-quartile regression. A positive link between globalization and carbon emissions, financial development and carbon emissions, and a negative link between financial development and carbon emissions were found. Ref. [40] analyzed the interactions amongst economic growth, energy consumption, carbon emissions, and environmental protection investment. The results revealed that energy consumption rises with economic growth, but energy consumption and economic growth increase carbon emissions simultaneously. Ref. [51] investigated the potentiality of economic growth, technological innovation, and renewable energy use on environmental sustainability using the Dynamic Ordinary Least Square method. Findings revealed that economic growth increases CO₂ emissions and fossil fuel emissions, while both renewable energy and technological innovation reduce carbon emissions. Additionally, ref. [52] examined the causal relationship between energy consumption and economic growth, and the results indicated a bi-directional relationship between energy consumption and economic growth and development. Ref. [53] empirically investigated the effect of agricultural development on carbon emissions in Ghana utilizing regression analysis and variance decomposition. The results demonstrated a U-shaped relationship between agricultural development and carbon emissions. Furthermore, ref. [3] investigated stringent environmental policies and their impact on carbon emissions using a quartile fixed-effect panel data approach. The findings indicated that an increase in policy stringency has a negative effect on emissions, but environmental stringency leads to lower carbon emissions. Ref. [5] explored the impact of economic policy uncertainty (EPU) on carbon emissions and proposed that countries should implement innovation and other environmentally friendly technologies, such as renewable energy, stringent tax policies to discourage non-renewable energy, and zero tax to encourage the usage of clean energy. Ref. [4] examined the impact of population growth, energy consumption, ecological footprint, and natural resources on carbon emissions using the Generalized Method of Moment (GMM), Generalized Linear Model (GLM), and Robust Least Squares (RLS). The results indicated that renewable energy and natural resources improve environmental quality in the long term, while non-renewable energy consumption and population growth are detrimental to environmental quality. Ref. [1] explored the link between fossil fuel energy consumption, industrial value-added, and carbon emissions in G20 countries using a panel CS-ARDL technique to determine the relationship among the variables. The results indicated that FDI, trade openness, government expenditure, research and development, and ICT are detrimental to carbon emission rates in G20 countries. Moreover, ref. [27] examined the impact of FDI, technological innovation, energy use, urbanization and financial development on carbon emissions among G8 nations. While a unidirectional causal relationship between FDI and carbon emissions was found, a long-run bi-directional causal relationship was found amongst trade openness, carbon emissions, financial development, economic growth, urbanization, and energy use. Ref. [54] assessed the effectiveness of policies in seven emerging countries’ panel data using the Augmented Mean Group (AMG). The results demonstrated an inverted U-shaped relationship between carbon emissions and environmental policy stringency. This implies that policy stringencies require time before becoming effective. They also found an unconditional causality between the two variables. Additionally, ref. [25] investigated the effectiveness of environmental taxes and environmentally stringent policies in carbon reduction within 20 European nations using a panel co-integration test, and the findings indicated that the higher the environmental stringency policy and the environmental taxes, the higher the carbon emission reduction.

Furthermore, ref. [30] empirically examined the association between inflation, GDP growth, and environmental stability utilizing the autoregressive distributed lag (ARDL) approach. The results indicated that the positive and negative shocks of inflation instability have distinct effects on environmental quality in both the short and long term. Inflation and GDP exhibited differential impacts on pollution emissions. Ref. [7] investigated the relationship between globalization and carbon emissions in India employing the Panel ARDL approach. The findings demonstrated that the acceleration of globalization and energy consumption resulted in increased carbon emissions. The study further revealed that globalization deteriorates environmental quality and accelerates economic and financial development in the long term. Additionally, ref. [37] explored the relationship between per capita income and environmental degradation using longitudinal data to estimate the Environmental Kuznets Curve. The results indicated that increasing levels of income per capita are associated with increased pollution and reduced environmental quality. Moreover, ref. [55] evaluated 22 independent variables utilizing two approaches, Bayesian Model Averaging and Weighted Averaging Least Square, with the findings revealing a positive ecological footprint. Ref. [38] analyzed the effect of economic growth on the performance of green logistics using the Panel GMM Two-Stage Least Square. The findings demonstrated that green logistics performance enhances the economic growth of OBRI, Central Asia, and MENA economies. Their results further indicated that while it exacerbates environmental pollution in MENA, OBRI, and Central Asia, it significantly improves environmental quality in Europe, the East, and Southeast Asian regions. Ref. [56] examined the interaction between renewable energy consumption, international trade, and environmental quality in Nordic countries. The study employed the dynamic common correlated effect (DCCE) and revealed that renewable energy improves environmental quality and is positively and significantly associated with international trade in the region. Ref. [39] investigated the impact of financial development on carbon emissions using Panel ARDL-ECM, and the results demonstrated a positive short-term and long-term relationship among financial scale, economic growth, and carbon emissions. Further results indicated a relatively small impact on carbon emissions. Ref. [57] analyzed air quality control, current air pollution, and future challenges in China, suggesting that China would continue to face more severe multiple pollutant emissions from energy consumption, electricity generation, vehicle population, etc., due to the continuing growth of its economy. The authors thus proposed a comprehensive control policy. Furthermore, ref. [58] investigated the impact of income inequality on carbon emission per capita in China using the GMM technique, and the findings indicated that carbon emission per capita increased as the income gap expanded. Ref. [42] examined the effect of financial development on carbon emissions for sub-Saharan African countries using GMM. The results revealed that financial development increases carbon emissions, while FDI, liquid liabilities, and domestic credit do not affect carbon emissions. Further results indicated that FDI moderates economic growth but does not reduce carbon emissions. Additionally, ref. [28] explored energy saving and the reduction of carbon to improve and safeguard China’s environment, revealing a reduction from 3.14% to 3.27% of carbon in 2007 compared to 2006 due to energy saving and the emission reduction policy adopted by China, with its corresponding GDP growth from 2.4% to 4%. Ref. [59] examined the dynamic impact of institutional quality on carbon emissions using the Panel GMM estimation technique. The empirical findings revealed that institutional quality reduced carbon emissions, thus improving environmental quality.

Since the economic boom in the 1970s, nations have endeavored to mitigate the adverse effects associated with environmental activities through policies aimed at maintaining an environmentally sustainable ecosystem [21]. Extant studies in this field have long established the relationship between carbon emissions and economic growth, financial development, environmental performance, FDI, and other factors, with most studies revealing a strong correlation. A conflicting interest between environmental quality, carbon emission control, and economic growth and financial development was established [28,55]. Few studies have established the relationship between or among these variables based on the extent of this study’s findings. Recently, the research focus has shifted to the control and mitigation of human impacts on the environment. Specifically, the existing literature has established the relationship between carbon emissions and financial development [39]; environmental quality and economic growth [17,37,38]; carbon emission and industrialization [39]; financial development and environmental quality [39,42]; environmental quality and globalization [7,16,39,47]; carbon emission and economic growth [37,40,60]; and carbon emission and renewable energy [28]. However, few studies have examined a bi-directional relationship between or among these variables. Additionally, research on carbon emission control and environmental quality is limited in the global literature. Therefore, this study contributes to the body of knowledge and expands the global literature to facilitate more accurate inferences and clarify existing conflicting findings. Other gaps identified in the literature suggest that the increase in carbon emissions is primarily attributed to weak policies and regulations surrounding the global implementation of laws related to carbon emission control [7]. There are also concerns regarding the overly ambitious nature of many national leaders and their apparent indifference towards pollution and emission control. They prioritize economic growth and development over environmental quality and emission control [40,47]. Another finding by [7] is the insufficient technology to transition from non-renewable energy consumption to renewable energy consumption, which results in high and difficult-to-control carbon emission rates, despite efforts to maintain them within acceptable limits. General findings from the review revealed that conflicting results could be country-specific factors and this could be the reasons for lingering carbon emissions control problem globally.

2.3. Data and Methodology

2.3.1. Data

This research analyzes the effect of pollution and carbon emission control on financial development and economic growth using a panel dataset for 65 economies spanning 2010–2021. The selection of timespans and countries is based on data availability from the World Development Indicators (WDI) see list of countries under Appendix A. For this analysis, the study employed E-views 10 version. The dependent variables of interest are economic growth (GDP), measured by GDP per capita, and financial development (FIN), measured by broad money (% of GDP). The explanatory variables of interest chosen are CO₂ emission (metric tons per capita); CO₂ as a proxy for pollution and carbon emission control; CO₂ emission from gaseous fuel consumption (% of total) (CGFC); population growth (POP); fossil fuel energy consumption (% of total) (FUE); and total greenhouse gas emission (kt of CO₂ equivalent) (TGRE).

Table 1. Variable description and data sources.

Variables	Description and Measurement Scale	Symbol	Source
Economic growth	GDP per capita growth (annual%)	GDP	WDI
Financial Development	Broad Money	FIN	WDI
Population	Population growth (total)	POP	WDI
Energy consumption	Fossil fuel energy consumption	FUE	WDI
Total greenhouse gas emissions	Proxy for environmental degradation	TGRE	WDI
CO2 emissions from gaseous fuel consumption	Pollution and carbon emissions control	CGFC	WDI

Note: WDI represents World Development Indicators. Source—Authors’ computation (2024).

below displays data source and their descriptions.

Table 2. Descriptive statistics. Summary of descriptive statistics and correlation matrix.

Variables	GDP	FIN	CGFC	CO2	FUE	POP	TGRE
Mean	36.115	2.252	4.521	1.740	27.965	235,821	34.024
Maximum	3.323	5.469	2.320	6.250	10.000	88.060	6.970
Minimum	1.490	5.109	3.427	1.218	8.078	47.899	7.314
Std. Dev.	49.338	6.322	3.475	12.049	34.024	96.075	14.445
Obs	993	993	993	993	993	993	993
Correlation matrix
GDP	1.000
FIN	0.423	1.000
CGFC	−0.524	−0.144	1.000
CO2	−0.711	−0.440	0.144	1.000
FUE	0.368	0.434	0.052	−0.056	1.000
POP	−0.605	−0.043	−0.016	−0.018	−0.097	1.000
TGRE	−0.312	−0.082	−0.030	−0.032	−0.159	−0.030	1.000

Source—Authors’ computation (2024).

displays the summary statistics and correlations of the variables under consideration. The average value of economic growth is 36.115 with a standard deviation of 49.338, indicating a wide variation in the selected countries. Similarly, the mean values of financial development and CO₂ emissions are 2.252 and 1.740, with a standard deviation of 6.322 and 12.049, respectively, signifying rising variability in the countries considered. Fossil fuel energy consumption and population growth average 27.965 and 235,821, respectively, with a standard deviation of 34.024 and 96.075, indicating high variation. The mean values of CO₂ gaseous emissions and total greenhouse emissions are 4.521 and 34.024, with a standard deviation of 3.475 and 14.445, respectively, indicating a lower variation. A correlation analysis is also shown in the lower panel of Table 2. The analysis indicates that all the variables are negatively correlated with economic growth, except financial development, which is positively correlated with economic growth. In addition, the analysis revealed no evidence of multi-collinearity amongst the variables, given that the series values were moderately low.

2.3.2. Empirical Model and Methodology

This study specifies the reduced-form empirical equation to estimate the effect of pollution and carbon emission control on financial development and economic growth in selected countries:

$$InGD{P}_{it}={\alpha }_0+{\beta }_{1}InCO{2}_{it}+{\eta }_{j}In{X}_{it}+{\epsilon }_{it}$$

(1)

where i = 1…83 and t = 2010…2021, $GD{P}_{it}$ represent the dependent variables including economic growth and financial development, $CO{2}_{it}$ denotes CO₂ emissions, $X_{it}$ represent the vector of other remaining explanatory variables, ${\alpha }_0$ is the constant, ${\beta }_i$ represents the coefficients of CO₂ emission, ${\eta }_j$ is the coefficient of other variables, and ${\epsilon }_{it}$ represents the error term.

This current research applies the fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) methods developed by [61,62] due to their outstanding properties. FMOLS and DOLS techniques are recognized as reliable and consistent estimators for cointegrated variables, which eliminates concerns related to endogeneity and serial correlations amongst the variables. In addition, the approaches are not restricted to a single integration of the order of variables. Specifically, the DOLS technique obviates multicollinearity and serial correlation by incorporating lead and first-lag differences into the cointegration regression. In [51], the authors asserted that the error term is orthogonalized when the leads and lags of distinct terms are incorporated. Additionally, the methods allow more flexibility when cointegrating vectors with heterogeneity in the presence of the between-dimension “group mean” [63]. Thus, the FMOLS proposed by [64] is expressed as

$${\widehat{\beta }}_{FMOLS}={\displaystyle \frac{1}{N}}{{\displaystyle \sum _{i=1}^N\left({\displaystyle \sum _{i=1}^T{\left(x_{i,t}-{\overline{x}}_i\right)}^2}\right)}}^{-1}\left({\displaystyle \sum _{i=1}^T\left(x_{i,t}-{\overline{x}}_i\right)y_{i,t}^{\ast }-T{\widehat{\gamma }}_i}\right)$$

(2)

where $x_{i,t}$ and $y_{i,t}$ are considered to be variables that are cointegrated with slope ${\beta }_i$ in order to account for individual specific effects. ${\widehat{\gamma }}_i$ corrects the serial correlation term due to the heterogeneity dynamics and $y_{i,t}^{*}$ is the transformed variable of $y_{i,t}$ to circumvent the endogeneity problem. Similarly, the DOLS approach also takes care of the correlation between regressors and error terms by adding a first-lag difference in the cointegrated relationships. Hence, the DOLS is expressed as

$${\widehat{\beta }}_{DOLS}={\displaystyle \frac{1}{N}}{{\displaystyle \sum _{i=1}^N\left({\displaystyle \sum _{i=1}^T{Z}_{i,t}{Z}_{i,t}^{,}}\right)}}^{-1}\left({\displaystyle \sum _{i=1}^T{Z}_{i,t}\left(y_{i,t}-{\overline{y}}_i\right)}\right)$$

(3)

where $Z_{i,t}$ denotes the 2(k + 1) × 1 vector of explanatory variables such as $\left(x_{i,t}-{\overline{x}}_i,\Delta x_{i,t-k}, \dots ,\Delta x_{i,t+k}\right)$.

Additionally, this study employed the Driscoll and Kraay standard error technique proposed by [65] to provide robustness to the outcomes of the FMOLS and DOLS approaches. The technique assumes that the error structure is heteroscedastic, autocorrelated up to some lag, and perhaps correlated between the panels. The technique is a robust and consistent estimate for obviating the concerns of cross-sectional dependency amongst the series, missing observations, and providing efficient outcomes, whether they are balanced or unbalanced panel datasets [66].

3. Causality Test

This study further explores the causal linkage between pollution, carbon emission control, economic growth, and financial development using the [67] causality test, which is robust, analyzes unbalanced panel data, and handles individual differences across countries [68]. Additionally, the approach analyzes z-bar statistics, taking into account regression coefficient variability. The method confirms a panel causality test based on the variability of the regression coefficients and non-causality mean across the cross-section units. The causality test is specified as follows:

$$Y_{it}={\beta }_i+{\displaystyle \sum _{n=1}^N{\alpha }_i^{(n)}}{Y}_{it-n}+{\displaystyle \sum _{n=1}^N{\gamma }_i^{(n)}}{X}_{it-n}+u_{it}$$

(4)

where Y and X represent the outcome and explanatory variables, respectively, and ${\alpha }_i^{\left(n\right)}$ and ${\gamma }_i^{\left(n\right)}$ denote the autoregressive and regression coefficients. Furthermore, the intercept term is represented by ${\beta }_i$, the lag operator is denoted by N, and the error term is symbolized by $u_{it}$.

4. Empirical Results

The current research begins the empirical analysis by inspecting the cross-sectional dependency (CSD) of the variables under consideration to avoid inaccurate results and choose appropriate approaches. Accordingly, the researchers employ the [69] CSD test to verify the CSD of the variables. The findings displayed in

Table 3. Cross-sectional dependence results.

Variables	Full Sample	Developed Countries	Emerging Countries
GDP	11.779 ***	8.085 ***	10.740 ***
FIN	2.593 **	4.398 ***	7.112 ***
CGFC	4.304 ***	6.135 ***	4.162 ***
CO2	9.441 ***	3.284 ***	8.965 ***
FUE	5.321 ***	7.048 ***	5.516 ***
POP	10.774 ***	9.552 ***	3.927 ***
TGRE	8.116 ***	5.691 ***	7.436 ***

Note: *** and ** denote the level of significance at 1% and 5% respectively. Source—Authors’ computation (2024).

show the presence of CSD amongst the variables in selected emerging countries, developed countries, and the full aggregate sample, as the null hypothesis of cross-sectional independence is rejected.

The next step is to examine whether there are slope heterogeneity issues using the technique developed by [70]. As depicted in

Table 4. Slope homogeneity test.

Slope Test	Model 1	Model 2	Model 3
$\widetilde{\Delta }$test	9.846 ***	7.138 ***	11.489 ***
$\widetilde{a`\Delta }$adj test	13.471 ***	10.014 ***	12.106 ***

Note: *** denotes the level of significance at 1%. Source—Authors’ computation (2024).

, the results indicate the absence of slope homogeneity, suggesting that the slope of the coefficient is heterogeneous in nature.

Having detected the existence of CSD, the use of a first-generation test might result in biased outcomes. Accordingly, a second-generation panel unit root test, ref. [71] CADF unit root, is employed to determine the integration order of the series. The findings of the unit root test are presented in

Table 5. Results of the CADF unit root test (full sample).

	Constant		Trend
	Level	First	Level	First
GDP	−0.194	−3.894 ***	−0.682	−3.573 ***
FIN	−1.489	−5.193 ***	−1.034	−4.381 ***
CGFC	−1.962	−4.897 ***	−1.261	−5.587 ***
CO2	−1.716	−5.091 ***	−1.641	−5.042 ***
FUE	−0.552	−3.972 ***	−0.620	−4.083 ***
POP	−1.146	−5.292 ***	−1.208	−5.730 ***
TGRE	−0.449	−4.500 ***	−0.514	−4.826 ***

Note: *** denotes the level of significance at 1%. Source—Authors’ computation (2024).

(Due to space conservation, the unit root test results of the full sample are only reported in this paper, but the other income country group results are available upon request from the authors). The results indicated that none of the variables are stationary at level, signifying that the variables contain unit roots. However, after the first difference, the variables become stationary.

The study proceeds further to ascertain the presence of long-run cointegration amongst the variables using the [72] panel co-integration test as the use of first-generation cointegration tests might generate biased outcomes. Thus, the researchers utilized the [72] panel co-integration test, which is robust in the presence of cross-sectional dependence and slope heterogeneity in the dataset, and estimated four statistics: two group mean tests ($G_t, G_a)$ and two panel mean tests ($P_t, G_a)$. Table 4 presents the results of the Westerlund Cointegration test. As shown in

Table 6. Results of the [73] panel cointegration test.

Statistics	1	2	3
$G_t$	−3.286 **	−5.312 ***	−4.220 ***
$G_a$	−7.104 ***	−9.847 ***	−6.438 ***
$P_t$	−9.337 ***	−11.017 ***	−9.281 ***
$P_a$	−5.892 ***	−6.135 ***	−5.842 ***

Note: *** and ** denote the level of significance at 1% and 5% respectively. Source—Authors’ computation (2024).

, the null hypothesis of no cointegration was rejected at the 1% significance level, indicating the presence of a long-run connection amongst the series.

Accordingly, the study proceeds to estimate the long-run coefficients of the variables using FMOLS and DOLS techniques. Table 6 presents the outcomes of the FMOLS and DOLS techniques for both income groups and the full sample based on DOLS (models 1–6) and FMOLS (model 1–6) techniques, where economic growth and financial development are used as the dependent variables. The environmental indicators are incorporated separately in the model specification primarily to avoid potential multicollinearity and also provide their respective impacts. The results show that the estimated coefficients of CO₂ emissions are negative and statistically significant in both income group countries and the full sample. This outcome highlights that increasing environmental pollution or degradation is inimical to the growth and financial processes of the selected emerging and developed income groups and the full sample. This finding also shows that the effect of CO₂ emissions is relatively higher in emerging countries compared to developed income group countries. This is not surprising, as the selected emerging countries exert more pressure on non-renewable energy resources, which emit environmental pollution and thus have an adverse effect on the growth and financial outcomes of the analyzed countries. This finding further implies that the ineffective control of environmental pollution poses a detrimental challenge to the economic growth trajectories of the considered countries. The results validate the argument of [73] that increasing carbon emissions puts negative pressure on economic growth, and economic growth is at the cost of environmental degradation. This result suggests that ineffective or inefficient control could lead to the increase in climate change across the globe [14,15]. This outcome is consistent with the findings of [33,47,74,75,76] in their studies.

Similarly, the estimated coefficients of CO₂ emission from gaseous fuel consumption are negative and significant across the income group countries and the full sample. This finding also shows that the marginal effect of CO₂ emissions emanating from gaseous fuel consumption is higher in emerging countries compared to developed countries that are already transitioning towards renewable energy consumption. This finding indicates that emerging countries need to put more controls in place to achieve the desired environmental quality in the region. This outcome also suggests that increasing CO₂ emissions from gaseous fuel consumption result in acid rain, leading to increasing climate change, low agricultural outputs, reduced labor productivity, and lower industrial outputs, consequently reducing the economic growth and financial performance trajectory of the analyzed countries. This outcome validates the findings of [41,77,78], who discovered similar results in their investigations. Furthermore, the estimated coefficients of total greenhouse gas emissions are negative in both income group countries and the full sample, but significant only in the considered emerging income group. This negative significance of greenhouse gas emissions may be explained by the negligence or reluctance of the analyzed countries to switch to modern and efficient (renewable) energy resources, which consequently slow down their production processes and thus affect growth and financial outcomes. Nonetheless, the insignificance of greenhouse gas emissions in developed and full-sample countries points out that increasing pollution emanating from greenhouse gas emissions has a negligible effect on the economic and financial development processes of the analyzed countries. Nevertheless, these findings highlight that policymaker still need to design formidable and efficient environmental quality control strategies to mitigate the increasing gas emissions in these countries. Past studies have revealed that greenhouse gases are major sources of climate change [9,14,15,18].

Conversely, the estimated coefficients of energy consumption are positive and statistically significant in both income groups and the full sample, with relatively slight differences between the two income groups. This outcome indicates that improvements in the accessibility and affordability of energy resources contribute significantly to the economic growth and financial development of the countries considered to be income groups. This finding further implies that making efficient use of the available energy resources enhances manufacturing productivity and generates more income opportunities for households, thereby contributing to the economic and financial performance of the analyzed countries. The finding is consistent with the outcomes of [41,78,79,80,81] in their investigations. The estimated coefficients on population growth, however, are negative across both income group countries and the full sample. This finding suggests that a rapidly growing population contributes to a rising pollution, carbon emissions, unemployment, and low employment, which consequently retard the economic and financial performance of the analyzed countries. The findings also imply that an explosive population leads to pressure on limited natural resources; reduced availability of capital; reduced efficiency of labor; food insecurity; and widening poverty, thus negatively affecting the economic performance of the analyzed countries. This finding conforms to the empirical works of [82,83,84].

This study further utilizes the Driscoll and Kraay standard error techniques to validate the consistency of the FMOLS and DOLS approaches.

Table 7. Panel DOLS and FMOLS estimation results for full sample.

Dependent Variable: GDP					Dependent Variable: FIN
DOLS Results
	1	2	3	4	5	6
CO2	−0.608 ***			−0.834 ***
	(0.001)			(0.000)
GCFC		−0.107 ***				−0.681 ***
		(0.000)				(0.000)
TGFE			−0.431			−0.177
			(0.128)			(0.319)
FUE	0.217 ***	0.198 ***	0.664 ***	0.151 **	0.618 ***	0.328 **
	(0.010)	(0.000)	(0.001)	(0.039)	(0.000)	(0.021)
POP	−0.157 ***	−0.311 ***	−0.414 ***	−0.471 ***	−0.127 ***	−0.729 ***
	(0.000)	(0.000)	(0.008)	(0.002)	(0.000)	(0.000)
FMOLS results	1	2	3	4	5	6
CO2	−0.962 ***				−0.752 ***
	(0.000)				(0.001)
GCFC		−0.548 **				−0.329 ***
		(0.031)				(0.000)
TGFE			−0.858			−0.551
			(0.173)			(0.248)
FUE	0.243 ***	0.372 **	0.267 ***	0.394 ***	0.447 ***	0.165 ***
	(0.005)	(0.040)	(0.000)	(0.016)	(0.031)	(0.000)
POP	−0.351 ***	−0.172 **	−0.390 ***	−0.194 ***	−0.889 ***	−0.371 ***
	(0.000)	(0.053)	(0.000)	(0.000)	(0.017)	(0.002)

Note: *** and ** indicate significance at 1% and 5%, respectively. Source—Authors’ computation (2024).

,

Table 8. Panel DOLS and FMOLS estimation results for developed countries.

Dependent Variable: GDP					Dependent Variable: FIN
DOLS Results
	1	2	3	4	5	6
CO2	−0.160 ***			−0.159 ***
	(0.000)			(0.003)
GCFC		−0.160 ***			−0.317 ***
		(0.001)			(0.000)
TGFE			−0.198			−0.136
			(0.538)			(0.274)
FUE	0.308 ***	0.218 ***	0.748 **	0.365 **	0.318 ***	0.129
	(0.000)	(0.000)	(0.031)	(0.050)	(0.000)	(0.000)
POP	−0.16 ***	−0.31 ***	−0.41 ***	−0.47 ***	−0.13 ***	−0.73 ***
	(0.000)	(0.000)	(0.008)	(0.002)	(0.000)	(0.000)
FMOLS results	1	2	3	4	5	6
CO2	−0.962 ***			−0.752 ***
	(0.000)			(0.001)
GCFC		−0.548 ***			−0.329 ***
		(0.031)			(0.000)
TGFE			−0.858			−0.557
			(0.173)			(0.248)
FUE	0.164 **	0.514 ***	0.108 **	0.130 **	0.583 **	0.159 **
	(0.023)	(0.000)	(0.042)	(0.000)	(0.000)	(0.040)
POP	−0.81 ***	−0.13 ***	−0.19 **	−0.92 **	−0.148 ***	−0.66 *
	(0.000)	(0.000)	(0.031)	(0.056)	(0.000)	(0.073)

Note: ***, **, and * indicate significance at 1%, 5% and 10%, respectively. Source—Authors’ computation (2024).

and

Table 9. Panel DOLS and FMOLS estimation results for emerging countries.

Dependent Variable: GDP					Dependent Variable: FIN
DOLS Results	1	2	3	4	5	6
CO2	−0.480 ***			−0.293 ***
	(0.000)			(0.000)
GCFC		−0.343 ***			−0.214 *
		(0.000)			(0.061)
TGFE			−0.213 **			−0.682 ***
			(0.041)			(0.000)
FUE	0.12 ***	0.93 ***	0.16 ***	0.12 **	0.16 ***	0.38 **
	(0.000)	(0.001)	(0.000)	(0.040)	(0.000)	(0.051)
POP	−0.61 ***	−0.13 ***	−0.12 **	−0.14 ***	−0.30 ***	−0.14 ***
	(0.003)	(0.000)	(0.039)	(0.000)	(0.000)	(0.010)
FMOLS results	1	2	3	4	5	6
CO2	−0.225 ***			−0.531 ***
	(0.000)			(0.002)
GCFC		−0.632 ***			−0.831 ***
		(0.000)			(0.004)
TGFE			−0.409 *			−0.839 *
			(0.062)			(0.073)
FUE	0.20 **	0.13 ***	0.32 ***	0.18 ***	0.45 ***	0.14 **
	(0.023)	(0.000)	(0.000)	(0.000)	(0.000)	(0.030)
POP	−0.46 ***	−0.71 ***	−0.24 **	−0.12 ***	−0.92 ***	−0.38 **
	(0.000)	(0.000)	(0.051)	(0.000)	(0.000)	(0.052)

Note: ***, **, and * indicate significance at 1%, 5% and 10%, respectively. Source—Authors’ computation (2024).

present the outcomes of the empirical analysis while

Table 10. Driscoll and Kraay results (robustness check).

Full Sample
	1	2	3	4	5	6
Variables	GDP			FIN
CO2	−0.507 ***			−0.109 ***
	(0.000)			(0.000)
CGFC		−0.758 **			−0.210 ***
		(0.023)			(0.000)
TGRE			−0.107			−0.240
			(0.248)			(0.112)
FUE	0.177 ***	0.589 ***	0.319 **	0.934 ***	0.561 **	0.610 ***
	(0.002)	(0.000)	(0.047)	(0.000)	(0.020)	(0.000)
POP	−0.363 ***	−0.317 ***	−0.184 ***	−0.177 **	−0.289 ***	−0.187 ***
	(0.000)	(0.003)	(0.000)	(0.051)	(0.000)	(0.000)
Developed countries	1	2	3	4	5	6
Variables	GDP			FIN
CO2	−0.248 ***			−0.156 **
	(0.001)			(0.023)
CGFC		−0.317 **			−0.196 ***
		(0.045)			(0.000)
TGRE			−0.696			−0.344
			(0.330)			(0.122)
FUE	0.261 **	0.171 ***	0.154 ***	0.268 ***	0.188 *	0.176 ***
	(0.030)	(0.000)	(0.002)	(0.000)	(0.079)	(0.000)
POP	−0.101 ***	−0.112 ***	−0.227 ***	−0.632 ***	−0.250 ***	−0.505 ***
	(0.006)	(0.000)	(0.001)	(0.000)	(0.010)	(0.000)
Emerging countries	1	2	3	4	5	6
Variables	GDP			FIN
CO2	−0.576 ***			−0.188 ***
	(0.000)			(0.000)
CGFC		−0.451 ***			−0.138 ***
		(0.017)			(0.000)
TGRE			−0.166 **			−0.298 ***
			(0.050)			(0.006)
FUE	0.301 **	0.126 ***	0.513 ***	0.373 **	0.431 ***	0.120 ***
	(0.027)	(0.000)	(0.000)	(0.029)	(0.000)	(0.000)
POP	−0.164 ***	−0.397 ***	−0.514 **	−0.407 ***	−0.189 ***	−0.668 ***
	(0.005)	(0.000)	(0.022)	(0.001)	(0.000)	(0.000)

Note: ***, ** and * denote the level of significance at 1%, 5% and 10%, respectively. Source—Authors’ computation (2024).

presents the robustness check by Driscoll and Kraay procedures. The results in the table also indicate that the three environmental indicators exert negative and significant effects on economic growth and financial development, respectively. However, the estimated coefficients of energy consumption and population growth are positive and negative, respectively, in both income group countries and the full sample. Overall, the findings from the Driscoll and Kraay techniques corroborate the results of the FMOLS and DOLS procedures, but differ in terms of coefficients and significance levels.

The researchers also explored the causal linkages between the variables using the Dumitrescu and Hurlin (D-H) causality tests.

Table 11. Causality test results for both income group countries and full sample.

Path of Causality	Full Sample	Developed Countries	Emerging Countries
$\mathrm{CO}2\ne \mathrm{GDP}$	6.191 ***	5.194 ***	8.392 ***
$\mathrm{GDP}\ne \mathrm{CO}2$	4.121 ***	1.248	5.061 ***
$\mathrm{CO}2\ne \mathrm{FIN}$	0.209	1.492	1.148
$\mathrm{FIN}\ne \mathrm{CO}2$	1.176	0.651	0.967
$\mathrm{CGFC}\ne \mathrm{GDP}$	8.614	5.334	6.170
$\mathrm{GDP}\ne \mathrm{CGFC}$	1.582	0.896	0.548
$\mathrm{CFGC}\ne \mathrm{FIN}$	10.791 ***	7.502	5.380
$\mathrm{FIN}\ne \mathrm{CGFC}$	0.861	0.861	0.593
$\mathrm{FUE}\ne \mathrm{GDP}$	4.087 **	5.355	3.228
$\mathrm{GDP}\ne \mathrm{FUE}$	0.149	0.171	0.341
$\mathrm{FUE}\ne \mathrm{FIN}$	7.137 ***	4.016 ***	5.169 ***
$\mathrm{FIN}\ne \mathrm{FUE}$	0.158	1.118	0.984
$\mathrm{POP}\ne \mathrm{GDP}$	1.403	1.038	1.112
$\mathrm{GDP}\ne \mathrm{POP}$	1.258	1.458	1.034
$\mathrm{POP}\ne \mathrm{FIN}$	0.125	1.046	1.296
$\mathrm{FIN}\ne \mathrm{POP}$	1.290	0.810	1.059
$\mathrm{TGRE}\ne \mathrm{GDP}$	7.427	1.583	5.411
$\mathrm{GDP}\ne \mathrm{TGRE}$	5.019	0.499	7.183
$\mathrm{TGRE}\ne \mathrm{FIN}$	1.285	0.517	1.539
$\mathrm{FIN}\ne \mathrm{TGRE}$	0.389	0.429	0.614

Note: *** and ** indicate significance at 1% and 5% respectively. Source—Authors’ computation (2024).

displays the D-H causality test outcomes for both income group countries and the full sample. The results show a bi-directional causality between CO₂ emissions and economic growth only in the emerging income group and the full sample, which suggests a feedback effect between the variables. This outcome suggests that economic growth is influenced by CO₂ emissions in emerging countries and full-sample countries. Alternatively, the increase in economic performance of the considered emerging income countries group and the full sample enhance CO₂ emissions. The results also show a unidirectional causality between CO₂ emissions from gaseous fuel consumption and fossil energy consumption, and economic growth and financial development, in both income group countries and the full sample. This outcome indicates that improved environmental quality control can accelerate the economic growth and financial development processes in both emerging and developed income group countries. Furthermore, a bi-directional causality between total greenhouse gas emissions and economic growth is detected only in emerging income countries and in the full sample. This outcome suggests that an increase in greenhouse gas emissions accelerates economic growth and vice versa. However, the findings show that there is no unidirectional or bi-directional causality from CO₂ emissions towards financial development, financial development to CO₂ emissions, or economic growth to CO₂ emissions from gaseous fuel consumption in all the considered income group countries and the full sample. The outcome implies that a neutral effect is discovered in causality tests running from CO₂ emissions to financial development in both income group countries and the full samples.

5. Study Implication

This study examined the effect of carbon emissions control on environmental quality and discussed the bi-directional relationship between carbon emissions and environmental quality, financial development, and economic growth. The empirical results show that the estimated coefficients of CO₂ emission are negative and statistically significant in all the model specifications and in both groups of countries and the full sample. This suggests that increases in carbon emissions and pollution have adverse effects on the economic growth and financial development of the analyzed countries. The finding also shows that the effect of CO₂ emissions is relatively higher in emerging countries compared to developed countries. This result suggests that ineffective or inefficient control could lead to the increase in climate change across the globe [14,15]. The analyzed result from total greenhouse gas emissions revealed a negative and significant result under emerging economies compared with developed economies that showed negative but insignificant results and justified the above assertion. Greenhouse gas is the main cause of climate change and suggests that emerging economies emit more greenhouse gases. This corroborates the findings of [85], who stated that human activities result in negative environmental impacts and propel climate change [9,14,15,18,35]. This is not surprising as the selected emerging countries exert more pressure on non-renewable energy resources, which emit environmental pollution and thus have an adverse effect on the growth and financial outcomes of the analyzed countries. By implication, the ineffective control of environmental pollution poses a detrimental challenge to the economic growth trajectories of the selected countries. This may further imply that carbon emissions and other forms of pollution are eco- and environmentally unfriendly and destroy the environment, which is a serious source of setbacks that can deny nations from attaining environmental sustainability. For instance, three top economies from the African continent are dominant consumers of coal (South Africa) and fossil fuel (Nigeria and Egypt), which are major sources of carbon emissions. The same also applies to China, which consumes coal in large quantities, more than any other energy sources. This suggests that the reason for the increase in environmental degradation around the world is evident and is attached to poor policy controls and the prioritization of economic growth and development over environmental issues by nations’ leaders. This also suggests a need for more stringent policies, laws, and regulations, such as environmental taxes and carbon emission taxes with strong implementation strategies, especially for emerging economies across the globe. Further implications from these results suggest that the development of technologically innovative policies that can counter all the devastating human activities on the environment are also needed. The results validate the argument of [73] that increasing carbon emissions put negative pressure on economic growth, and economic growth is at the cost of environmental degradation. This outcome is consistent with the findings of [33,35,47,74,75,76] in their studies.

Similarly, the estimated coefficients of CO₂ emissions from gaseous fuel consumption are negative and significant across the models and the income group countries and the full sample. This finding also shows that the marginal effect of CO₂ emissions emanating from gaseous fuel consumption is higher in emerging countries compared to developed countries that are already transitioning towards renewable energy consumption. This outcome suggests that increasing CO₂ emissions from fossil fuel consumption such as carbon monoxide from coal, diesel, and petrol are detrimental to the growth and development of the global economy. By implication, the rate of pollution from this source needs serious attention and, if not properly controlled, would further destroy the quality of the environment. The increase in carbon emissions has also resulted in increasing health issues, climate changes, and low agricultural outputs in the long-run, leading to food insecurity, reduced labor productivity, and low industrial outputs, consequently reducing the economic growth and financial performance trajectory of the selected countries. This outcome validates the findings of [41,77,78,86], who discovered similar results in their investigations. Furthermore, the estimated coefficients of GHG emissions are negative in both income group countries and the full sample, but significant only under the emerging income group. This negative significance of GHG emissions may be explained by the negligence or reluctance of the analyzed countries to switch to modern or more efficient energy resources, which consequently slow down their production processes and thus affect growth. Nonetheless, the insignificance of GHG emissions in developed and full-sample countries points out that increasing pollution emanating from GHG emissions has a negligible effect on the economic and financial development processes of the analyzed countries. On the other hand, the negative and significance result may suggest that increasing the concentrations of GHG emissions into the environment is detrimental not only to lives, but also to economic growth and development. Increases in GHG result in climate changes and acid rain, which increases surface temperatures and has other harmful effects on the surface of the earth. All these are unfriendly to the environment and to the entire ecosystem. By implication, this leads to poor outputs of agricultural produce and to low productivity from farmers, thus causing food inflation and leading to global food insecurity. It may not have a significant effect on the economic growth process of the analyzed countries immediately, as was revealed from the result, but would surely have a long-run effect on the entire ecosystem [9,15,18]. Policymakers, however, still need to design formidable environmentally friendly quality control policies to ameliorate the increasing gas emissions in these countries. This is consistent with the findings of [2,51].

Conversely, the estimated coefficients of energy consumption are positive and statistically significant in all the estimated models and in both income groups and the full sample. This outcome indicates that improvements in the accessibility and affordability of energy resources to all contribute significantly to the economic growth and financial development of these countries. This further implies that making efficient use of the available energy resources enhances manufacturing productivity and generates more income opportunities for households, thereby contributing to the economic and financial performance of the analyzed countries. This finding indicates that increasing the consumption of energy contributes to the economic growth and financial performance of the analyzed economies. Moreover, this finding further points out that making efficient use of energy in the analyzed countries is a crucial driver of economic growth. By implication, increases in energy consumption must be backed by high policies, laws, and regulations with sound follow-up to achieve maximum carbon control that can maximize environmental quality, especially for non-renewable energy consumption. This further implies that no carbon energy is very germane, considering how important energy is to global growth and development. The finding is consistent with the outcomes of [51,79,80,81,87] in their investigations. The estimated coefficient of population growth is negative and statistically significant, suggesting that a rapidly growing population contributes to rising unemployment and low employment, which consequently retard the economic and financial performance of the analyzed countries. The findings also imply that explosive populations without proper and adequate policies lead to pressure on limited natural resources; reduce the availability of capital; reduce the efficiency of labor; create food insecurity; widens poverty; and thus, negatively affects the economic performance of the analyzed countries. By implication, uncontrolled population growth affects pollution and carbon emissions across the globe. This finding conforms to the empirical works of [82,83,84] and opposed the findings of [88]. Conclusively, the result from bi-directional relationships amongst the variables generally revealed that economic growth is influenced by carbon emissions and, conversely, carbon emissions are also a major determinant of economic growth, especially when adequate control is in place. By implication, this proves that the effective control of carbon emissions would enhance economic performance across the globe, which would later transform to global economic growth and development.

6. Policy Recommendations and Conclusions

This study investigates the impact of carbon emissions on environmental quality; examines the relationship between carbon emissions and environmental quality, financial development, and economic growth; and proposes policy recommendations. The findings from the analysis indicate that the estimated coefficients of carbon emissions, specifically fossil fuel consumption, carbon dioxide, greenhouse gases, and population, are negative and statistically significant across the model, with the exception of greenhouse gas emissions, which yielded insignificant results. This demonstrates that an increase in carbon emissions and other forms of pollution are detrimental to environmental quality, economic growth, and financial development. Consequently, the ineffective control of environmental pollution and carbon emissions presents a significant challenge to the economic growth trajectories of the selected countries. For instance, three leading economies on the African continent are the predominant consumers of coal (South Africa) and fossil fuels (Nigeria and Egypt), which are major sources of carbon emissions. China consumes substantial quantities of coal, surpassing other energy sources. These findings may inform nations under study regarding the selection of appropriate policy measures. The results from the directional relationships revealed bi-directional causality between CO₂ and GDP and between total greenhouse gas emissions and economic growth, with no directional relationship between CO₂ emissions and financial development, and vice versa, and economic growth to CO₂ emissions from gaseous fuel consumption, and vice versa. However, a unidirectional relationship was observed between fossil fuel consumption and financial development, carbon emissions from gaseous fuel, and GDP and financial development, and vice versa, indicating that enhanced environmental quality control can facilitate global economic growth and financial development. Consequently, policymakers should formulate robust environmental quality control policies to mitigate the increasing gas emissions in these countries. This study provides insights for governments, policymakers, international organizations, researchers, and various stakeholders. More stringent fiscal and monetary policies, laws, and regulations, such as environmental and carbon emission taxes, with effective implementation strategies, particularly for emerging economies, are strongly recommended globally. Further recommendations suggest the development of technologically innovative policies that can counteract the detrimental impact of human activities on the environment. The increased consumption of renewable energy and the utilization of environmentally friendly innovative technologies that can help control carbon emissions are encouraged worldwide. The strong bi-directional associations between carbon emissions and economic growth suggest that countries should exercise caution when pursuing their economic targets. This study is cross-country-based. Further studies should consider country-specific factors that may influence environmental assessments.