The Impact of the Financial Industry and Globalization on Environmental Quality

Abstract

The objectives of this study were to evaluate the impact of financial development, globalization, and pollution in six MENA countries from 1971 to 2015. Many prior studies empirically explored numerous factors determining environmental quality/pollution across the world. As far as the region of North Africa and the Middle East is concerned, the majority of previous studies ignored the combined role of globalization and financial development in predicting environmental quality using carbon emissions (CO₂). Furthermore, we aimed to assess the legitimacy of the environmental Kuznets curve (EKC) theory for MENA nations. For this purpose, a feasible generalized least squares (FGLS) estimator was applied. It was found that the development of the financial sector and globalization significantly affected ecological quality. Regressors such as energy use and foreign direct investment (FDI) had an altogether positive effect on natural quality. These empirical discoveries also demonstrate the acceptability of the EKC hypothesis for MENA nations. This study shows that governments in the Middle East and East Africa need to develop and implement appropriate policies to promote renewable energy sources, such as wind, solar, biofuel, and heat production, which help to reduce carbon dioxide emissions and promote sustainable economic development.

1. Introduction

It is beyond doubt that global temperatures are on the rise and climate change is speeding up. Thus, it is of great importance to consider the issues from socio-political and economic angles. In 1997, the Kyoto Convention introduced limits on greenhouse gas (GHG) emissions, which cause changes in the atmosphere. This includes carbon dioxide (CO₂), which is at its highest level in modern times, and is a cause of global warming. Natural resources are disappearing quickly, which will create shortages for the next generation. Large numbers of people, particularly in vulnerable sections of society, have been badly affected. Every country strives for economic growth in order to elevate the living standards of their people; however, the increased use of resources further complicates environmental issues.

Globalization can be defined as the process that allows the business community, governments, and other entrepreneurs across the globe to interact with each other for economic purposes. In the 1970s, the idea of encouraging business growth, increasing capital modernization, and expanding and conserving cultural, political, and social exchange was developed. Globalization, along with its key policies, has paved the way for the economic, sectarian, and cultural development of nations. It also has significant environmental implications at the regional and international levels. The new era is defined by globalization, because advancement and innovation in business are related to air quality and pollution [1,2]. The results show that financial growth exacerbates environmental degradation. It is noted that globalization has a key role in financial development [3]; however, only a few studies demonstrate that financial development has a significant number of positive effects. In this view, modernization fails to reduce the level of environmental discharge. For this reason, we assessed financial development in this study. Many studies establish a positive nexus between energy usage and CO₂ emissions, and show that economic development can be achieved by efficient and effective productivity [4,5,6]. It is also accepted worldwide that increasing energy use will result in the release of harmful gases [7]. Figure 1 shows CO₂ emissions, and financial globalization development in the MENA (Middle East and North Africa) countries.

2. Environmental Kuznets Curve (EKC) Hypothesis

In 1995, Simon Kuznets proposed the environmental Kuznets curve, which explains the relationship between per capita income and various environmental degradation indicators. It is a hypothesis that claims that, during the early stages of economic growth, environmental pollution increases with the increase in per capita income. Moreover, there comes a level where the trend reverses, after which improvement in per capita income begins to show improvements on an environmental level [9]. This hypothesis negates the argument that high income definitely leads to high pollution, because an increase in income tends to result in more demand for products or services, which results in more production and pollution in the end. However, the case may be different for countries in which technologically advanced machinery is used for production in order to cope with pollution, because advanced technology can be less polluting as it is specifically designed to be energy-efficient. In addition to technology, the demand for better environmental quality increases with increasing income; for example, people demand less polluting products and want to live in a clean environment, pressurizing the governments to implement environmental laws [10]. Work carried out on the environmental impacts of NAFTA emphasize the rise of the EKC hypothesis [11]. The beginners who came with a complete significant report to accept the EKC theory [12]. This study examined EKC theory in the chosen MENA locations via econometric investigations.

Economic development and environmental release behavior indicate that, when growth begins, before it has reached a certain level, it has a negative impact on the environment. Then, economic development safeguards the environment when a specific level of per capita income is achieved. Moreover, another factor, FDI (foreign direct investment) inflows, is relevant to this investigation. FDI inflows have rapidly expanded over the previous decades in every region. It can be seen that FDI inflows can have a positive effect on diminishing carbon emissions [13]. According to this study, the core of this is population development. Population mass is a critical factor in terms of developing the social and personal skills to decrease emissions. This also decreases emissions by creating new societal awareness. The association between financial growth, globalization, and CO₂ emissions is an important area of study; however, these variables are rarely the focus of study. For example in [14], the authors investigated the connection between economic progress, energy usage, and toxic emissions in the MENA nations. The outcomes demonstrated a bidirectional association by utilizing the Cobb–Douglas production equation. The influence of illegitimacy on contamination and per capita income of 21 nations from the MENA area from 1996–2013 was assessed by utilizing the dynamic panel data model. The researcher found a link between EKC speculation and CO₂. The impact of globalization in the MENA nations, from an ecological perspective, ought to be the focus of study [15].

Literature surveys show that few studies focus on the MENA countries [6]. This study considers the MENA region. There are many reasons for selecting this region, such as these countries having more than 40% of the world’s natural gas. In contrast, 57% of the world’s oil reserves come from the region, representing approximately 85% of the world’s greenhouse gas emissions. The atmosphere is getting disturbed because of the financial grant on petroleum products [16]. Furthermore, there are many natural disasters in these countries related to carbon emissions. CO₂ emissions are higher for most MENA countries than other areas. Therefore, it is critical to discover the connection between CO₂ emissions and globalization, which includes financial progress as a factor [17]. This study is the latest to empirically explore the relationship between globalization, the financial development sector, and environmental degradation in six selected MENA countries between 1971 and 2015 based on the EKC hypothesis. We used the Globalization Index (KOF) to build a fixed-effect model in order to obtain realistic and unbiased results (R3 Q3). In this study, we also used the most appropriate analytical methods to assess the heteroskedastic and automatic data associated with feasible generalized least squares (FGLS).

The present investigation aimed to discover the impact of globalization and economic growth on CO₂ emissions and applied the EKC hypothesis for six MENA nations: two high-pay nations (Saudi Arabia, Israel), two upper-income nations (Jordan, Iran), and two middle-income nations (Tunisia, Egypt), from 1971 to 2015.

This paper is arranged as follows. Section 2 explains the applicability and authenticity of the chosen variables. Section 3 presents the data and empirical methodology. Section 4 provides the results and discussion. Section 5 concludes the study.

3. Literature Review

In previous studies, it was noted that “Sustainable economic growth needs to be the primary objective of every government, including developing Asian countries, to improve the social welfare of the people [1]. Therefore, to achieve the desired level of sustainable economic growth, environmental degradation needs to be controlled without lowering real growth and negatively affecting the well-being of society.

The association between globalization, financial development, and CO₂ has been analyzed in various studies that applied the same methodology to developing economies [18]. The above variables are used for OECD countries [19]. The relevant literature is discussed in the section below in order to compare and discuss the existing literature.

3.1. Carbon Emissions and Globalization

The literature regarding the relationship between CO₂ and globalization is not particularly extensive. The effects of globalization and financial growth on CO₂ emissions in APEC countries were assessed in a study by Westerland [20]. C-shell quadruple used globalization and economic development to reduce CO₂ emissions in the short term and long term [20]. Another study showed that there is a relationship between globalization and CO₂ emissions, and the EKC hypothesis was used for 87 countries [21]. Data from 166 nations, from 1990 to 2016, demonstrated the link between CO₂ emissions and globalization [22]. They showed that globalization is important in environmental destruction, even though OECD and non-OECD nations may exhibit differing after-effects. Globalization and the fundamentals of environmental deficiency in MENA nations were assessed from 1980 to 2013. The technical ARDL panel was used to evaluate the results [17]. Globalization, energy use, and financial development in MENA countries were shown to be in line with environmental standards. The political, social, and economic factors of globalization, and their influence on CO₂ emissions, were studied and compared to European countries from 1995 to 2015, utilizing the AMG method in the long term. It was concluded that politically aware globalization reduces CO₂ levels, and economic and social degradation reduces CO₂ levels [23].

3.2. Carbon Emissions and Financial Development

An investigation using the EKC hypotheses found that FDI, trade, and renewable energy influence carbon emissions, with renewable energy decreasing CO₂ emissions, FDI reducing carbon emissions, and trade tending to increase CO₂ emissions [24,25]. This study was based on Pakistan’s economy under the ARDL simulations model. It assessed the influence of financial factors on carbon emissions, with the results showing that FDI, energy usage, and trade have a positive influence on carbon emissions and have the same influence on financial, social, and political indices [26,27]. It was shown that financial growth, and green contamination by CO₂ release, had a significant influence on four BRICS countries from 1981 to 2015 [7].

3.3. Carbon Emissions and Economic Growth

The environmental Kuznets curve is a globally used model to determine the relationship between national income and CO₂ emissions. An experimental analysis was performed using the CMI-PM metric panel fix effects model for G7 nations, to examine the link between CO₂ emissions and globalization from 1970 to 2015. CO₂ emissions, the KOF globalization index, energy usage, and GDP were used. They reported a negative U-shaped link between globalization and CO₂ emissions in support of the EKC, and economic growth was also significantly connected with CO₂ emissions [28]. The reversed EKC U-shaped link was used in the long term to investigate the connection between environmental excellence and growth in per capita income [29].

As economic growth increases, environmental growth declines, but any further improvement in economic growth after a certain limit will improve environmental quality. In 15 MENA countries, the author explored the relationship between economic growth, CO₂, and energy usage. This study shows that both in the short and long term, long-term CO₂ and growth connectivity led to energy stabilization [30]. In contrast, short-term carbon emissions and energy consumption had no relationship with energy use or growth. A national-level analysis was carried out for 22 MENA countries using time-series data to analyze the environmental Kuznets curve for GDP and environmental degradation. Environmental examples such as SO₂ (sulfur dioxide) and CO₂ (carbon emission) were used [31]. EKC was not supported in this area; however, it provided varied results on the kingdom scale. For countries that supported EKC, certain economists used panel datasets for the EKC [19]. OECD countries were also examined. A complete explanation of the EKC assumption for Malaysia for the periods 1985–2012 and 1971–2012 was obtained with the help of a modified set of repressors [32]. Three Mediterranean nations, specifically Portugal, France, and Spain, from 1992 to 2014 were investigated, and the EKC hypothesis was verified by targeting the agriculture sector [33].

3.4. Carbon Emissions and Energy Usage

It was revealed that financial progress, CO₂, and energy practice in the MENA zone exhibited a unidirectional link with energy consumption; however, a bidirectional link was observed between CO₂ and monetary growth [14]. The link between energy usage and emissions will lead to increased revenue in APEC nations [2]. Moreover, CO₂ emissions were used to investigate the N-shaped relationship between income and emissions in APEC countries [34].

3.5. Carbon Emissions, Foreign Direct Investment (FDI), and Population Size

Changes in population affect the environmental Kuznets curve (EKC). In one study, the influence of FDI on CO₂ emissions for different population sizes is revealed, with carbon emissions having an influence on maximum, minimum, and moderate population sizes. The increase in CO₂ emissions is significantly related to FDI [35]. Energy usage and income positively affect carbon emissions; on the other hand, CO₂ emissions have a negative relationship with the square of income. This is in accordance with the EKC hypothesis, which shows an inverted U-shaped effect between CO₂ emissions and economic development. Energy consumption, FDI, and income were shown to be important factors for CO₂ emissions in Vietnam [36] from 1981 to 2010. Panel data were used from five Asian countries with the PMG perimeter to investigate the influence of FDI, income, and energy usage on CO₂ emissions. FDI was shown to reduce environmental well-being. Moreover, development and energy usage were shown to have a negative influence on the environment [37].

A panel of 54 countries, for the period 1990–2011, was used to investigate the links between CO₂ emissions, FDI, and economic development, using dynamic simultaneous-equation panel data models [38]. CO₂ emissions and economic development were significantly related, with CO₂ emissions and FDI exhibiting a common process and economic development and FDI exhibiting the same process. The axis of the environmental agreement is valid for China and Indonesia. The pollution paradise hypothesis is valid for China, India, Indonesia, Iran, and South Africa. In this study, they found that CO₂ emissions had a positive effect on energy consumption. In Indonesia, foreign direct investment increased CO₂ emissions, and the use of clean and modern energy technology boosted pollution levels on an industrial scale. In a study of the selected 44 SSA countries from 1984 to 2006, wasteland co-integration was used, with CO₂ emissions as the dependent variable, and real GDP, urbanization, globalization, and energy poverty as the independent variables. The results show that globalization was negative and insignificant. On the other hand, the remaining variables exhibited a positive and significant relationship with CO₂ emissions [39]. From 1995 to 2014, in the BRICS countries, FMOLS was used to assess CO₂ emissions as the dependent variable, and economic development, financial development, globalization, energy use, and urbanization as independent variables. Energy consumption and financial development were shown to have a positive (+vie) effect and globalization and urbanization a negative (−vie) effect [40].

3.6. The Effect of the Financial Industry and Globalization on Environmental Quality

Environmental pollution is a huge problem in every country [41].

Saudi Arabia needs green energy for sustainable development. They aim to use renewable energy to increase social and industrial development and carbon emissions. In addition, the long-term imbalance between economic growth and renewable energy in the first phase, and renewable energy and environmental quality through carbon emissions, which lead to globalization, in the second phase are key factors in shaping Saudi Arabia’s economic policy and CDS action plan [42,43]. Several studies have been carried out to explore the relationship between renewable energy, CO₂ emissions, and real GDP using linear regression (

Table 1. Prior studies on the association between globalization, financial development, and environmental degradation due to CO₂ emissions.

Authors	Countries, Method, and Time	Dependent Variable	Independent Variables	Findings
Khan et al. (2020) [45]	Pakistan, ARDL 1965–2015	Carbon emissions	Economic growth, coal, oil and gas use	Energy use has a positive influence on carbon
Salahuddin et al. (2019) [39]	44 SSA countries Westerlund cointegration 1984–2006	Carbon emissions	energy poverty, urbanization, real GDP, globalization	Globalization was (−ive) and insignificant and other regressors were (+ive), and there was a significant relationship with emissions
Haseeb et al. (2018) [40]	BRICS Countries, FMOLS 1995–2014	Carbon emissions	Energy consumption, economic growth globalization, urbanization, financial development,	Energy consumption and financial development were positively associated, globalization and urbanization were negatively associated
Audi and Ali (2018) [16]	MENA countries Panel ARDL 1980–2013	Carbon emissions	Globalization, energy consumption, population density, per capita income	Independent variables had a (+ive) and significant connection with CO2 emissions	CO2 emissions	Energy consumption, per capita income, globalization, and population density	All the independent variables exhibited a positive and significant relationship with CO2 emissions
Charfeddine and Mrabet (2017) [17]	15 MENA countries Pedroni, cointegration, FMOLS, DOLS 1975–2007	Ecological footprint variable (proxy for environment degradation)	Urbanization, energy usage, real GDP,	U-shaped behavior in all non-oil-exporting countries, GDP and urbanization had a reverse relationship with the dependent variable
Shaari et al. (2017) [46]	Malaysia, ARDL1971–2013	Carbon emissions	GDP, energy usage	In the long term, electricity and economic growth had a significant impact on the environment
Bu et al. (2016) [22]	166 countries 2SLS, fixed-effect 1990–2009	CO2 from industrial and construction sector and GHG, CO2	KOF globalization, per capita GDP and manufacturing value added to GDP	As carbon emissions increased, so did globalization. Carbon dioxide in industrial development and construction was negatively related to globalization
Saidi and Hammami (2015) [47]	58 Countries, GMM 1990–2012	Energy use per capita (ENRC)	Financial development, labor force, GDP, CO2, capital stock, population	GDP, CO2, and financial development were positively associated
Munir and Khan (2014) [5]	Pakistan, VEC 1980–2010	Fossil fuel energy consumption and CO2	Real GDP, financial development, trade openness, and industrial value-added population, investment, exports and imports	Negative effects on carbon dioxide. Industrial and commercial value added had a positive effect on CO2. Importers and manufacturers negatively affected final consumption
Al-Mulali et al. (2013) [48]	MENA countries Pedroni cointegration, DOLS 1980–2009	Entire prime energy consumption	Urban population and total carbon dioxide emissions	Long-term bidirectional (+) connection between the variables

); however, certain selected studies focused on the unbalanced relationship between these variables. Thus, this study investigated the disproportionate causal relationship between REC, real GDP, and CO₂ emissions in KSA [44]. The main goal was to find a balance. It was concluded that the negative feedback associated with CO₂ emissions is beneficial for the actual GDP in the long term but not in the short term. A positive short-term and long-term effect on real GDP is not the same as a negative impact on renewable energy, which indicates that there was an inconsistency between the short-term and long-term effects on the renewable energy in both the long- and short-term.

3.7. Data and Empirical Methodology

It was concluded that the negative feedback of CO₂ emissions, which leads to environmental quality, is beneficial for the actual GDP in the long term but not in the short term (Figure 2). A positive short-term and long-term impact to real GDP is not the same as a negative impact to renewable energy, which indicates that there was an inconsistency between the short-term and long-term effects on renewable energy in both the long- and short-term [43,49].

4. Methodology

In this study, we were concerned with factors such as FDI inflows [50], financial development, globalization, CO₂ emissions, population, and economic growth by GDP. On the other hand, other important areas relate to different forms of the EKS theory. As a result of data availability, we focused on the population from the MENA region in the period 1971–2015. We considered Israel and Saudi Arabia as higher-wage countries, Iran and Jordan as middle-wage countries, and Egypt and Tunisia as lower-wage countries between 1971–2015. Mainly to the CO₂ release, the low-salary countries and the people who have no concern with the economy of the world have been banned. There was also a problem related to adjusting the information for the low-pay nations. Details were obtained from the WDI (World Development Indicators) and World Bank for the year 2020. Details concerning globalization were obtained from the records of KOF for those years (Dreher 2006) [8]. The details in

Table 2. Data and sources.

Variables	Definition	Supporting References	Sources
Carbon emissions	Metric Tons per capita	Omri (2013) [15]	WDI, 2020
Globalization	Index	Zafar et al. (2019) [29]	KOF index
Financial development	% of GDP	Al-Mulali et al. (2013) [51]	WDI, 2020
Per capita GDP	Current USD	Bilgili et al. (2016) [52]	WDI, 2020
Square of per capita GDP	Current USD	Bilgili et al. (2016) [52]
Energy use	Kilogram of oil equivalent per capita	Zaidi et al. (2019) [7]	WDI, 2020
FDI inflows	% of GDP	Shahbaz et al. (2019) [19]	WDI, 2020
Population size	Total population	Dong et al. (2018) [53]	WDI, 2020

were adopted to build the globalization list, based on political, economic, and social globalization.

4.1. Empirical Model

The beliefs cited in the Northern America Free Trade Agreement (NAFTA) are the basis of globalization, as has been illustrated in multiple studies [11]. When the outcome increases through foreign trade and investments, it is known as the globalization scale effect. The globalization scale effect is the increase in the outcomes as result of foreign trade and investments. There are various effects associated with this, and two were particularly important in this study: the technique and the composition scales of globalization. This occurs due to the scale effect of globalization. With physical variations, the environment can damage the globalization scale effect. Moreover, with physical variations, the environment can become damaged. This may be due to composition effects resulting from the increase in investment in the production sector [8]. Whenever both the scale effect and economic growth become equal, concepts related to new methods of production come about, which is known as the globalization technique effect. This overall process reduces CO₂ emissions, for investment and trade that is pollution free.

To explore the various relationships with carbon emissions, a measure of financial development was used. Financial development demonstrates that CO₂ emissions have an important role in any society. Some enhancements, specifically monetary improvements, protect the “consumers” approach and counts for product consistency, which is seen to increase environmental production and energy usage [54]. Financial advancement is one of the variables that provides a chance for the professional community to increase its capital, which helps in expanding manufacturing practices. It was demonstrated that national tariffs in the private sector, as increased according to the real GDP, are demonstrative of quantitative financial increase without economic development and energy use. They have a global impact and represent financial expansion that cannot be measured, and the majority of discussions do not include these well-considered aspects [55]. The discussion regarding CO₂ integration and economic growth is mainly focused on EKC, for example, in APEC countries [56]. This study focuses on measuring carbon emissions for low-, medium-, and high-wage countries from different parts of the world.

CO₂ = ƒ (GLOB, FD, GDP, GDP2, EUSE, FDI, PS)

(1)

In Equation (1), carbon emissions are denoted as CO₂ and calculated in metric tons. There are many sources of CO₂ emissions (gas, coal, oil, and fuels). Financial development, which is abbreviated as FD, is the percentage of gross domestic product (GDP) and is used as a proxy for broader “domestic credit issued to the Private sector” per capita. Gross domestic product is represented by GDP. Currently, the United States of America’s currency, the dollar, is a proxy for economic growth. The square term of per capita GDP is expressed through GDP squared. Measuring the EKC up-to-date US$ is key. Energy usage is represented as EUSE. For all energy usage, the parameter used is kilograms of oil equivalent to per capita energy. Foreign direct investment is abbreviated as FDI and denotes the inflow calculation as the percentage of GDP. PS represents the population of midyear estimates.

There is an institute for economic research known as the Swiss Institute. They established a globalization index known as the KOF index. This index is further divided into three groups, namely, political, social, and economic indexes. The KOF globalization index includes all factors. The KOF range is from 0 to 100. The GLOB variable, mentioned in the aforementioned model, is a globalization index from the KOF globalization index. Keeping in mind data sensitivity, we converted the variables in the model into a log form. The transformation method is the most widely used technique for transforming data to give evidence of familiarity [57].

LCO₂et = β0 + β1e LGLOBet + β2e LFDet + β3e LGDP_et + β4e[LGDP²]et + β5e LEUSEet + β6e LFDIet + β7e LPS_et + εet

(2)

where e represents the number of countries, t shows the quantity of time, ε is the error term, and β1, β2, β3, β4, β5, β6, β7, and ε are the coefficients of globalization, financial development, economic growth, economic growth square, energy use, foreign direct investment inflows, population growth, and the error term, respectively.

4.2. Estimation Method

A cross-sectional dependence test for panel data is used all over the world by many researchers. Problems are mainly caused by issues related to the surrounding space, similar exchange designs, or similar financial practices and methods. Therefore, if we ignore this issue, it can produce good results. By using the CD (cross-sectional dependence) test [51,52] and the LM test, [53] as shown in Appendix A, the outcomes were significant at the 1% level. Thus, we can accept the alternative hypothesis.

Appendix B shows the consequences of all the variables currently utilized. The outcomes show that a few factors are motionless on a level, and some are stationary at the first difference. Therefore, the consequence of the unit root test is the combined command of the stationery aspect. The second-generation test was used for the investigation [51]. When the statistics are examined in order to perform the root test for this unit, the test depends on the cross-section of the unit. Invalid assumptions indicate that the information is independent [58].

Appendix C shows that the p-values are highly insignificant, which means that no panel co-integration was found. When co-integration is not found, it shows that choosing a voting method would be the best way to examine the link between variables, and the optimal and clear answer in order to select the fixed effect or random effect. For the Hausman test, [59] researchers have several options to analyze the panel data, but the most important option is the ordinary least square (OLS). Specifically, researchers give priority to using fixed-effect (FE) or panel-corrected standard (PCS) errors [53]. Panel data modeling has a time and group effect for dealing with personal impacts and various inflationary effects, and these effects are either a default effect or random effect [60].

The work of Breusch and Pagan (1980) was used to ensure the best outcomes in cross-sectional independence in the fixed-effect regression [61,62]. The test was used for autocorrelation to avoid autocorrelation issues in the panel data [63], and ‘Parks’ feasible generalized least squares (FGLS) estimator [64] was used for the same autocorrelation issues. However, it is important that this is applied when the volume of the cross-section does not correspond to the volume of time [61]. The Wald test was applied for a heteroskedasticity group-wise examination of fixed-effect regression. Feasible generalized least squares (FGLS) was used to avoid heteroskedasticity [61].

5. Results and Discussion

Table 3. Descriptive statistics.

Variables	Mean	Minimum	Maximum	Std. Deviation.
LCO2it	1.352821	−0.438635	2.967367	0.8760321
LGLOBit	3.865844	3.318839	4.386682	0.7174191
LFDit	3.588851	1.011817	4.505349	0.6629812
LGDPit	8.009402	5.496376	10.53685	1.168841
LEUSEit	7.115166	5.380083	8.840111	0.8265699
LFDIit	.4147791	−1.29133	1.394486	0.4413644
LPSit	7.153587	6.257482	7.965872	0.4861979

shows the fundamental descriptive statistics. CO₂ emissions were common in MENA countries from 1971 to 2015, with Figure 1 showing that 352,821 metric tons were released. The lowest GDP in terms of economic growth was 5.49%. The highest level of GDP was 10.52%. As regards globalization, the standard was 3.37. The standard FD was 3.59%. The minimum and maximum speeds of individual energy use were 5.38 kg and 8.84 kg, respectively. However, FDI inflows were 0.414% in the data from the selected countries.

Table 4. Correlation matrix among variables.

Variables	LCO2it	LGLOBit	LFDit	LGDPit	LEUSEit	LFDIit	LPSit
LCO2it	1.0000
LGLOBit	0.0566	1.0000
LFDit	−0.1594	0.3514	1.0000
LGDPit	0.8604	0.2762	0.1722	1.0000
LEUSEit	0.9545	0.1886	0.0190	0.7481	1.0000
LFDIit	0.3019	0.1335	−0.1697	0.1152	0.2688	1.0000
LPSit	−0.0070	−0.2015	−0.2897	−0.1352	0.0384	0.1574	1.0000

shows information concerning the correlation matrix. The positive link between CO₂ outflows and globalization [39] also shows that globalization had a positive and significant influence on CO₂ emissions, CO₂ and GDP, energy usage and CO₂, and FDI and CO_2. The results show a positive connection with globalization and financial growth, financial development and energy use also have a positive connection. Moreover, FDI exhibited a positive link with population size. On the other hand, financial development had a negative association with CO₂, and financial development, population size, and CO₂ were negatively related to globalization and population size.

Fixed-effect and random-effect models were utilized for panel data estimation, and the Hausman test was applied to identify the most efficient and proportional models. The fixed-effect model calculates time-changing properties, i.e., characteristics that exist in individual countries. These individualities are specified for a single country. When we used the FA test, we assumed that something inside the body was creating a bias in our descriptive variables. In an attempt to expose that, we used the Hausman test.

The hypothesis of the Hausman test is as follows:

H_0. 

The random effect model is appropriate;

H_1. 

H₀ is false.

Under the null hypothesis, the random effect model is shown to be a good choice. On the other hand, the alternative option shows that the fixed-effect model is the best option in terms of identifying the relationship between variables.

Table 5. Fixed-effect estimator results.

Variables	Coefficient and p-Values
LGLOBit	−0.0937539 *** 0.000
LFDit	−0.1149732 *** 0.004
LGDPit	0.9038601 *** 0.000
LGDP2it	−0.0451319 *** 0.000
LEUSEit	0.5106622 *** 0.000
LFDIit	0.0957134 *** 0.007
LPSit	0.0524394 0.703
R-square	0.94
Hausman test	27.51 *** 0.000

Note: *** show the 1% significance level.

shows the outcomes of the fixed-effect model for the panel data. It is clear that the Hausman test rejected the null hypothesis and accepted the alternative option. Thus, according to the Hausman test, the fixed-effect model produced the best results. However, before interpreting the results of the fixed-effect model, certain diagnostic tests were performed, such as group-level hygroscopicity, serial correlation, and contrast-independent tests, in order to ascertain whether the default model was valid. The following diagnostic tests were used to assess the fixed-effect model’s reliability.

Table 6. Under the fixed-effect model cross-sectional independence test.

H0: Cross-Sectional Independence
Breusch–Pagan LM test: chi2 (15)	74.370, Pr = 0.0000

shows the outcomes of the Breusch–Pagan LM tests of cross-sectional independence. The probability value was less than a 5% significance level, showing that there is a cross-sectional dependency in these panel data under the fixed-effect model. The null hypothesis was accepted at a 1% significance level.

Table 7. Groupwise heteroskedasticity test.

Modified Wald test	chi2 (6)	p-Value
	477.96	0.0000

shows the modified results of the WALD test for group-level heterocyclic in the fixed effects regression model [65]. The probability value was less than a 5% significance level, so data under a fixed-effect model exhibit heteroskedasticity at the 1% significance level.

Table 8. Autocorrelation test.

H0: No First-Order Autocorrelation
Wooldridge test	F(1,5)	Probability
	11.256	0.0203

shows the Wooldridge test, which was used as the autocorrelation diagnostic test for the fixed-effect model as the null hypothesis was rejected. It is shown that the data present an autocorrelation issue under the fixed-effect model. The outcomes of the diagnostic tests show that we have heteroskedasticity and an autocorrelation structure. Thus, we were not able to use the fixed-effect model to interpret the outcomes. As the fixed-effect model was not appropriate, we used cross-sectional time-series feasible generalized least squares (FGLS) regression.

The outcomes of FGLS regression are shown in

Table 9. Cross-sectional time-series FGLS regression.

Coefficients	Generalized Least Squares
Panels:	Homoskedastic
Correlation:	No autocorrelation
Variables	Coefficients	p-values
LGLOBit	−0.1255702 ***	0.000
LFDit	−0.2644918 ***	0.000
LGPDit	0.6248269 ***	0.000
LGDP2it	−0.0286917 ***	0.000
LEUSEit	0.8216209 ***	0.000
LFDIit	0.098522 ***	0.000
LPSit	−0.1728335 ***	0.000
Wald chi2 [7]	8464.41	0.000

Note: *** show the 1% significance level.

.

Table 9 shows the relationship between globalization and CO_2, which is negatively significant. Our results are in accordance with those in [20,40], i.e., they show that if globalization increased by 1%, emissions are reduced by 0.1254% in the selected MENA countries. Carbon emissions and financial development were shown to be significantly negatively correlated. Green illnesses improved by 0.2644% when financial development increased by 1%. Our study has similar outcomes to those reported in [19,28]. The EKC mode is explained by the GDP and GDP square [20], which also exhibited the same results. A direct relationship exists between carbon emissions and economic growth (GDP), which shows that the environment is polluted by 0.6248% if there is a 1% increase in GDP. Moreover, a GDP square increase of 1% leads to 0.0286% lower emissions. Thus, GDP square and CO₂ have an indirect relationship with each other. These results show that carbon emissions also increase in the first phase as economic growth increases, but CO₂ emissions decrease over time when economic growth reaches a certain level. These results are also supported by Azam and Khan (2016b) [66]. Moreover, the existing results are in accordance with the EKC hypothesis in the selected MENA countries (Israel, Saudi Arabia, Iran, Jordan, Egypt, and Tunisia) for the period from 1971 to 2015.

CO₂ and energy consumption were shown to be significant and have a positive relationship. The results show that emissions will increase by 0.8216% as a result of a 1% rise in energy usage in the MENA countries (Figure 3). The after-effects of FDI inflows show that the relationship with carbon emissions is somewhat deeper and more important. The study explains that a 1% increase in FDI will pollute the environment by 0.0985%. Our experimental results are similar to those of previous studies. There is a significant and indirect correlation between population size and carbon emissions. It can be seen in the results that CO₂ emissions reduced by 0.17283% when the population size increased by 1%.

Policy Implications

According to the findings, we propose various recommendations that will help the MENA countries to generate effective policies. The existence of the EKC curve for MENA countries suggests that these countries should maintain sustainable economic growth. In this manner, the reverse phenomenon of the EKC curve will be achieved, i.e., a decrease in carbon emissions. Globalization in the selected countries leads to a reduction in carbon emissions. With this in mind, we suggest that the MENA region needs to improve the banking sector, control corruption, and curb property rights violations. This will ultimately reduce unemployment and generally increase globalization. The private sector is also key to reducing unemployment, and this will lead to reducing the poverty level in the selected regions. Hence, the government needs to focus on privatization while keeping in mind the aforementioned scenarios.

Moreover, the private sector should initiate environmental projects because it has a direct relationship to financial development, and is key to reducing environmental pollution. Energy consumption in the MENA region needs attention, and the role of energy consumption in environmental pollution must be explored. The MENA region should formulate and implement policies to encourage the use of renewable energy sources, such as wind, solar, biofuel, and thermal materials, to reduce CO₂ emissions. Energy consumption should be reduced in the selected MENA countries. Energy savings can be achieved by introducing energy-saving plans and energy-preservation strategies in the selected MENA region. Foreign direct investment can create difficulties in the MENA region if CO₂ emissions are not controlled in these countries. Therefore, to efficiently utilize FDI, government officials should create policies that lead the way to a clean environment. Furthermore, to create environmental standards and prevent pollution, they need to encourage the transfer of systematic information and secure technology through foreign investors.

The government should take note that population size and CO₂ emissions have a strong effect on each other. This cannot be controlled by modifying population figures. Officials should focus on important regulations related to population size in MENA countries in an effort to highlight population and emission issues.

6. Concluding Remarks

This study aimed to analyze the effect of financial development and globalization on CO₂ outflows in six nations in the MENA region. These were classified into three categories based on income levels, i.e., high-income countries (Saudi Arabia, Israel), middle-income countries (Iran and Jordan,) and lower-income countries (Tunisia and Egypt), for the period from 1971 to 2015. For this purpose, a feasible generalized least squares (FGLS) estimator was utilized to gauge the outcomes. According to the results, the existence of an EKC for the selected countries suggests simultaneous improvement in terms of GDP. On one hand, financial development and globalization need to be improved by facilitating business, curbing property rights violations, and enhancing privatization. On the other, FDI should be encouraged, but under the strict surveillance of environmental protection agencies. Governments need to ensure energy efficiency in order to control extra energy usage. Moreover, these countries need to move towards the use of renewable energy to control harmful emissions.