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https://doi.org/10.56556/gssr.v3i4.974  

                                                                  

 

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RESEARCH ARTICLE  

Measuring How AI Innovations and Financial Accessibility Influence 
Environmental Sustainability in the G-7: The Role of Globalization with 
Panel ARDL and Quantile Regression Analysis 

Shewly Bala1, Sarder Abdulla Al Shiam2, S M Shamsul Arefeen3, Shake Ibna Abir4, Hemel Hossain5, 

Md Sibbir Hossain6, Shaharina Shoha4, Afsana Akhter7, Mohammad Ridwan7*, Sumaira8 

 

1Department of Finance, University of Dhaka, Dhaka 1000, Bangladesh 
2Department of Management -Business Analytics,St Francis College, USA 
3Master of Science in Business Analytics, University of Massachusetts Boston, USA 
4Department of Mathematics, Western Kentucky University, Bowling Green, Kentucky, USA 
5Dhaka School of Bank Management, University of Dhaka, Bangladesh 
6Department of Computer Science, The City College of New York, Convent Ave, New York, NY 10031, USA 
7Department of Economics, Noakhali Science and Technology University, Sonapur, Noakhali-3814, Bangladesh 
8College of Economics and Management, Zhejiang Normal University, Zhejiang China 

 

Corresponding author: Mohammad Ridwan. Email: m.ridwan.econ@gmail.com 

Received: 12 August, 2024, Accepted: 21 October, 2024, Published: 29 October, 2024 

 

Abstract 

This study investigates the impact of AI innovation on environmental sustainability in the G-7 region from 2010 

to 2022. Additionally, it tests the Load Capacity Curve (LCC) hypothesis in relation to financial accessibility, 

globalization, and urbanization. Cross-sectional dependence and slope homogeneity tests reveal the presence of 

cross-sectional dependence and heterogeneity issues. Panel unit root and panel cointegration tests confirm that 

the variables are free from unit root problems and are cointegrated in the long run. To identify significant factors 

influencing environmental sustainability, this study employs Panel ARDL and Quantile Regression methods. 

Both methods confirm the LCC hypothesis in the G-7 region, demonstrating a U-shaped relationship between 

income and the load capacity factor. The results indicate that AI innovation and financial accessibility are 

significantly positively correlated with the load capacity factor, while globalization and urbanization are 

negatively correlated, leading to lower environmental sustainability. To validate the robustness of the Panel 

ARDL and Quantile Regression results, Driscoll-Kraay standard errors, Augmented Mean Group, and Common 

Correlated Effects Mean Group estimation approaches are applied, all of which support the initial findings. 

Furthermore, the D-H causality test reveals unidirectional causality from economic growth, financial accessibility, 

globalization, and urbanization to the load capacity factor, and bidirectional causality between AI innovation and 

the load capacity factor. 

Keywords: Artificial Intelligence; Financial Accessibility; Globalization; LCC Hypothesis; G-7 region 

  

Introduction

The sustainability of natural assets of the G-7 countries is a notable and ongoing concern, given that all of the 

members, except Canada, have environmental imbalances (Global Footprint Network, 2019). As the G-7 

countries contribute to more than 60% of the world's net global wealth through their extensive economic activity, 



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it is obvious that emissions from fossil fuels, coal, and conventional cooking fuels are the major cause of pollution 

(Alola et al., 2022a). While several G-20 and G-7 states continued to grow commercially during the last ten years, 

the serious threat that climate change possess to ecosystem integrity continues to be one of the most significant 

challenges (Alola et al.,2022b; Hossain et al.,2023). According to UNEP (2019), mitigation in pollutions below 

25% and 55%, respectively, is necessary to meet the goal of global humidity level less than 2 °C and 1.5 °C by 

2030. The worldwide average temperature has spiked by 0.4 to 0.8 degrees Celsius over the past several decades, 

and by 2100, it could climb by 1.4 to 5.8 degrees Celsius (Danish et al. 2020). As the G7 contributes to 27.3% of 

global emission of carbon, they do, address enormous environmental difficulties. Remarkably, the inquiry finds 

that the United States, Germany, the United Kingdom, and Japan are the G7's biggest pollutants (Zheng et al., 

2019). The Group-7 territory offered a major improvement to the management of the globe's climate and 

attempted to diminish the rate of climate change by putting different policies into place through public and 

commercial institutions (Song et al.,2021).  In light of this, this research intends to explore the consequences for 

load capacity factor (LCF) in the G-7 areas of GDP, Financial Accessibility (FA), Artificial Intelligence 

Innovation (AI), Globalization (GOB), and Urbanization (URBA). G-7 was selected for several considerations. 

With a considerable 39% share of the global economic output and 10.981 billion tons of emissions of carbon 

dioxide, the nations of the G-7 possess a major influence on the global economy. Nonetheless, figuring out what 

causes global warming is essential (Dastgeer et al., 2023). As a result, the economies of these nations bear a 

considerable degree of responsibility for environmental degradation. In addition, environmental damage 

continues to remain a risk to the G-7 countries even with their progress toward a green economy (Khan et al. 

2020). For example, the group generated around 38% of the total world emissions between 1960 and 2014 (World 

Bank 2017). Thirdly, among the numerous elements that contribute to environmental contamination in the region, 

the ongoing advancements in the global value chain provide further grounds for concern (Ibrahim & Ajide, 2021; 

Mithun et al.,2023; Faruk et al.,2023). A more accurate environmental evaluation can be obtained by the 

LCF (Siche et al., 2010). It reflects a country's ability or capacity to sustain its people following their 

contemporary lifestyles (Xu et al., 2022). Thus, an ecosystem is considered sustainable when its LCF is larger 

than one and unsustainable when it is less than one (Pata et al., 2021). Consequently, implementing consideration 

of the aforementioned rationale, this research will hold significant policy implications for decision-makers 

concerning sustainable development goals (SDGs). Concerns addressing the possible negative effects on human 

development emerge as technological developments, particularly in artificial intelligence (AI), transform the 

community (Qin et al., 2023). Artificial intelligence (AI) is a general term for several kinds of devices and 

platforms that replicate human intelligence and perform activities without human intervention (Sohail et 

al.,2018a; Sohail et al.,2018b; Saba & Monkam, 2024). It is a powerful instrument for boosting efficiency, 

effectiveness, and creativity because of its possible advantages in areas like automation, data analysis, and 

decision-making (Makridakis 2017). Artificial intelligence-driven commercialization is projected to reach $3.9 

trillion in 2022, up from $1.2 trillion in 2018, which marked a 70% growth from 2017 (Richards et al., 2019). 

The G-7 countries actively made investments in AI technologies, enacting regulations, establishing institutes for 

research, and assisting startups because they acknowledge the  potential of AI in industries like medical sector, 

farming, finance, and others (Cyman et al. 2021; Dukhi et al. 2021).Understanding the interplay among all these 

factors in this particular environment is crucial for designing  modern strategies that focus on capitalizing on the 

advantages of AI integrated progress in the G-7 countries. The widespread prevalence of globalization illustrates 

the interdependence of nations, with foreign direct investment and international commerce significantly 

influencing the economic dynamics (Jahanger et al. 2022; Ozturk and Ullah 2022). Numerous environmental 

consequences of globalization are visible on an interpersonal and global scale. Technological innovation can 

develop as an outcome of globalization and lessens the ecological impact (Akadiri et al., 2020).  However, a key 



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contributing element to inadequate green growth is the uncertainty underlying economic policy (Khan et al., 

2019). The GDP of the G-7 nations is expected to reach 60.1 trillion US dollars (USD) in 2021, accounting for 

44.1% of the world's GDP (WB, 2022). Out of all the G7 nations, the United States has the greatest GDP with a 

wide margin. In addition, the GDP of the United States rose almost continuously between 2000 and 2022, 

surpassing the GDPs of the other six countries combined to reach an estimated 25 trillion dollars in 2022. 

Before China, the United States had the greatest economy in the world. At over 4.2 trillion US dollars, Japan's 

GDP was the second biggest among the G7 (Dyvik, 2023). Several studies have been done to figure out the factors 

that contribute to environmental pollution, and many of them point to economic expansion as an important 

variable in the degrading of the natural world (Ozcan and Ozturk 2019; Schröder and Storm 2020). According to 

Bhattacharyya (2018), Ahmed et al. (2020), Shah et al. (2019), and Wu et al. (2020), decreasing economic growth 

has lowered carbon emissions since 2012. This investigation provides numerous important contributions to the 

existing body of knowledge. First off, most of the research that is currently available to assess the effects of 

LCF has merely looked at one or two of the consequences of globalization, artificial intelligence innovation, or 

financial accessibility, neglecting to take all three into account. Second, when assessing ecological damage, the 

LCF offers a more sophisticated approach than the Ecological Footprint (EF). Due to this, we decided to employ 

the LCF as an endogenous variable. Furthermore, there is a shortage of information in the literature about the 

applicability of the LCC hypothesis in developing countries, such as the G-7 nations. Our work fills this grasp by 

exploring the LCC hypothesis' applicability to the G-7 countries, which makes it a special contribution to the 

field. This might be partially explained by the inconsistent findings of the earlier empirical research. Third, even 

with the theoretical and empirical data supporting the idea that innovation in AI regulates the adverse effects of 

various toxins in the environment, these types of concerns are still relatively new, especially for developed 

countries such as the G-7. Fourth, by endeavoring to investigate the tripartite effects of GOB, FAI, and AI on 

environmental quality, this research is also novel. Lastly, we use a strong and contemporary econometric approach 

by using the most recent data available for long- and short-term estimations from 1990 to 2019 and performing 

panel unit root tests based on first- and second-generation methods, quantile regression, cross-section dependence 

tests, and the ARDL method. Additionally, we used AMG, CCEMG, and DKSE estimates to confirm their 

robustness.  

Following is the structure of the relevant study sections: In part 2, there is a thorough representation of the 

literature comprising related investigation summarized extensively. The third portion covers the topics and 

methodology; the fourth subsection includes the outcomes and discussions; and the final part contains the 

conclusion and its policy proposal. 

 

Literature Review 

Many empirical analyses have addressed at the consequences of globalization, financial accessibility, AI 

innovation, and economic development on the load capacity factor (LCF). The majority of research has 

concentrated on how urbanization, green energy use, and advances in technology affected environmental quality; 

however, numerous analyses have made use of the ARDL framework. The link between financial globalization, 

financial advancement, economic growth, and LCF has been examined in other research; nonetheless, the quantile 

regression approach has attracted less attention in those investigations. The literature on ecological deterioration 

in the G-7 countries is still in the early stages and lacks comprehensive research. However, a few earlier studies 

have provided direction for the factors and research techniques chosen. A handful of such inquiries will be 

examined in this section. A rising income level will allow the expansion of the LCF, improve environmental 

quality, and maintain the LCF CURVE within the ASEAN region (Dai et al., 2024). Lin and Ullah (2024) 

performed an analysis in Pakistan using the time-series data from 1970 to 2021 and an advanced dynamic 



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Autoregressive Distributed Lag (DARDL) approach. They discovered that the LCF decreases by 0.027 % for 

each 1% boost in economic development. In the top nuclear power economies, growth in GDP has a detrimental 

impact on the LCF dynamics that drive ecological degradation (Teng et al., 2024). Using methodologies for 

second-generation panel data, Sun et al. (2024) investigate the factors that impact the LCF in 17 APEC countries. 

The results of this research imply that ecological health declines with economic growth. In their analysis of G7 

and E7 countries between 1997 and 2018, Khan et al. (2023) observed a link within economic growth and a 

decline in the LCF. According to multiple studies (Huilan et al.,2024; Ozcan et al.,2024; Du et al.,2024; Awosusi 

et al.,2022; Pata and Isik,2021; Das and Sethi,2023; Ahmad et al.,2024), GDP growth has a detrimental influence 

on LCF and lowers the quality of biodiversity. But when Solarin et al. (2021) employed the ARDL approach for 

Nigeria between 1977 and 2016, they discovered that although growth in the economy initially degrades the 

environment, it eventually improves it over time. However, Jahanger et al. (2023) discovered that LCF is 

favorably influenced by GDP expansion in the top SDG countries. Between 2007 and 2014, Ameyaw and Yao 

(2018) investigated that here was no evidence of causation between CO2 emissions and gross fixed capital 

creation, based on the study. Similar to this, Nathaniel et al. (2020) investigated how growth in the economy 

affected the EFP in CIVETS territory by utilizing the AMG estimator. They concluded that GDP 

growth isn't harmful to biodiversity. Moreover, Raihan et al. (2024a) also observed similar outcomes in India. On 

the other hand, Onwe et al.(2024) revealed that economic development has varied consequences on environment 

condition in Japan. Digital technology and artificial intelligence are being utilized progressively to enhance 

strategies for lowering CO2 emissions from human activity. A variety of industries, including CO2 disposal, 

depend on machine learning models for improved productivity. Because classical approaches are obscure and 

hard to understand, bankers continue to utilize them despite advances in artificial intelligence (Ferdous et al.,2023; 

Shiam et al.,2024a; Arif et al.,2024). A variety of industries, including CO2 disposal, depend on machine learning 

models for improved productivity (Shiam et al.,2024b; Rana et al.,2024). Several investigations (like Rahman et 

al.,2024; Abir et al.,2024) expressed that the major effect of artificial intelligence (AI) technology on raising 

standards for sustainability and effective marketing, particularly machine learning (ML), deep learning (DL), and 

big data. AI encourages sophisticated, efficient, and environmentally friendly industrial structures (Sohail et 

al.,2019) which have an influence on CO2 emissions (Yuan et al., 2016). Shiam et al.(2023c) considered an 

examination in Nordic region from 1990 to 2020 to analyze the association between AI innovation, urbanization, 

GDP, stock market capitalization and banking improvement. By incorporating the STIRPAT framework they 

concluded that advancement in AI has inverse association with ecological footprint in the selected area. Similarly, 

Ridwan et al.(2024b) performed an analysis in USA from 1990 to 2019 to check the implication of AI on natural 

health. They made use the ARDL technique and illustrates that AI related technology can ensures ecosystem 

sustainability. In G-7 area Ridwan et al.(2024c) conducted another research by using MMQR method to see how 

AI innovation affect the LCF. Their result demonstrated that application of AI has advantageous consequences 

on the ecosystem level. Additionally, Akther et al.(2024) explored a study in USA by adopting the ARDL bound 

test covering data from 1990 to 2019. They observed that private funds in AI has favorable link with LCF. 

Furthermore, Hossain et al.(2024) in Nordic region also aligned with this findings. 

Using a variety of econometric methods, an in-depth examination of the complex connection between 

globalization and its implications on ecosystems has been carried out extensively. Utilizing a long-run time series 

dataset spanning from 1970 to 2021, Wang et al. (2023) demonstrated that environmental deterioration is 

negatively and severely impacted by globalization in China. When Hasseb et al. (2018) examined an insignificant 

but negative correlation between the two factors. Usman et al. (2020) explore how environmental damage is 

caused by globalization within the framework of South Africa's EKC and discovered that ecological damage is 

reduced as a result of GOB. Shahbaz et al. (2017) employed the ARDL bounds test technique from 1970 to 2012 



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together with the Bayer and Hanck combined cointegration analysis. They discovered that China's CO2 emissions 

are significantly lower as a result of globalization. In opposition to the findings of these inquiries, Ulucak and 

Erdogan, (2022) claimed that in the cases of 78 developing and OECD nations, the GOB had a detrimental effect 

on environmental pollution. Using an examination of the implications of globalization, GDP, and digitization, Li 

et al. (2023) observed at how the next eleven countries boosted their LCF between 1990 and 2018. Using the CS-

ARDL approach, the long-term outcomes illustrate that reliance on globalization reduced LCF. Wenlong et al. 

(2022) showed through the use of the QARDL technique that GOB leads to an acceleration of ecological 

excellence in the United States. Additionally, several investigations have demonstrated that globalization has an 

encouraging effect on ecosystem damage (Jahanger et al., 2022; Sadiq and Khan, 2022; Sheraz et al., 2022; Wen 

et al., 2021). The foundation of all other types of advancements and businesses is a robust financial expansion, 

all of which is required to generate revenue for the finance sector. On the other hand, it has been demonstrated 

that there is a statically uncertain association between ecological and financial expansion (Sharif et al., 2024). 

Scholars assume that an even more expanded financial sector might potentially enhance the standard of living for 

individuals globally. The number of financing possibilities that become accessible could grow as the banking 

sector develops larger (Tamazian et al. 2009; Tamazian & Rao 2010). To investigate the effect of financial 

accessibility on CO2 emissions from 1990 to 2019, Raihan et al. (2024b) carried out research in the G-7 territory. 

The results of the Panel ARDL model indicate that financial accessibility (FA) worsens the environment and 

raises CO2 emissions in the G-7 region. Additionally, FA boosts assets and earnings by generating affordable 

financing, boosting diversification of risks, and promoting company stability that leads to job creation. This 

expansion in turn increases the consumption of power which causes to CO2 emissions and degrades the 

environment (Acheampong 2019; Sadorsky 2010). Boussaidi and Hakimi (2024) suggest that policymakers must 

enhance the standards of their institutions to promote growth, avert the detrimental effects of accessibility in 

finances, and safeguard ecological diversity in the MENA area. On the other hand, Gao et al. (2024) evaluate the 

importance of financial accessibility in the context of environmental pollution for the E-7 nations. The results 

highlight the positive effects of financial inclusion on carbon emissions and the significance of this policy for the 

sustainability of the environment. In five South Asian economies, Islam (2022) discovered that because there is 

a direct link between financial development and CO2 emissions but the latter does not diminish with the 

development of financial accessibility. The goal of people transferring from rural to urban locations is to have 

typical lives while working in industries that generate revenue (Ruel et al., 2008). Urbanization encourages the 

need for transport and manufacturing, increases the use of oil and gas, and enhances the environmental impact 

(EFP) (Ulucak and Khan 2020). Within the context of the LCC theory, Fang et al. (2024) examine the impact of 

political risk, biomass utilization, and natural resources on the LCF in ASEAN nations. The ARDL estimator's 

output demonstrates how urbanization lessens LCF, and the LCC curve is verified in Thailand. The ARDL 

approach is used by Raihan et al. (2023b) to do research in Mexico using data spanning from 1971 to 2018. The 

findings show that urbanization lowers Mexico's LCF, which reduces the quality of the environment. 

Additionally, they advocate for Mexican authorities to endorse an ecologically conscious socioeconomic strategy 

and promote sustainable urban growth. Moreover, urbanization may boost residents' spending power, which will 

influence their desire for renewable energy sources and decrease EFP (Danish and Wang 2019). Lin and Ullah 

(2024) observed that in Pakistan, a one percent rise in urbanization improves the LCF by 0.029 %. The 

relationship between CO2 emissions and urbanization in the BRICS economies was analyzed by Zhu et al. (2018). 

According to their results, urbanization lowers emissions and enhances the quality of the natural world. 

Furthermore, Danish et al. (2020) agreed with the findings that urbanization enhances the standard of ecosystems 

in the BRICS area using the FMOLS and DOLS methodologies. Multiple studies, including Ali et al. (2017) 

within Singapore, Raggad (2018) in Saudi Arabia, and Saidi and Mbarek (2017) for 19 nations, corroborate the 



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aforementioned conclusions. However, Raihan et al.(2022a) and Voumik and Ridwan (2023) opposed this 

findings and concluded that population growth harms the biodiversity. 

In the end, our review of previous research has demonstrated that there aren't lots of works that particularly 

investigate the LLC hypothesis for the G-7 nations while accounting for the consequences of globalization, 

financial accessibility, and AI advancement. Although the LLC hypothesis has been examined in developing 

nations by various studies, their analysis has been limited and has not considered the effects of other areas of the 

economy. Given that the G-7 countries are a rapidly emerging territory with distinctive macroeconomic and 

environmental features, it is sense to test the LLC hypothesis. Moreover, improvements in AI might support 

sustainable behaviors, minimize problems with the environment, promote energy efficiency, and assist agriculture 

all of which in turn could decrease the danger of climate change. From the G-7 perspective, these features make 

artificial intelligence (AI) an entirely novel area for study. This strategy makes it possible to estimate panel data 

models efficiently, which enhances the methodological understanding in the field. By examining these 

procedures, the selected nations might be able to assess if utilizing innovations in technology, financial 

cooperation, and sustainable development might offer the possibility to improve its LCF and improved 

sustainability. Therefore, filling in this gap in the literature might enhance our knowledge of how economic 

progress and environmental damage interplay in the group seven countries while having an enormous effect on 

the long-term sustainability of the region's policies.  

Methodology 

Data and Variables 

This study sought to explore the intricate connections between GDP, urbanization, financial accessibility, 

artificial intelligence (AI), globalization, and LCF for the G-7 countries. By adopting sophisticated econometric 

techniques, the investigation intended to evaluate the LCF hypothesis and get an understanding of the intricate 

interactions that exist across these variables. LCF, the dependent variable in the study, was taken from the reliable 

Global Footprint Network (GFN, 2022). The World Development Indicators (2022) provided the GDP, GDP 

squared, and urbanization data, while trustworthy resources such as Our World in Data, the Global Financial 

Inclusion Index, and the KOF Globalization Index provided the information on artificial intelligence, financial 

accessibility, and globalization. To provide a thorough summary of all characteristics examined, including their 

definitions, sources, and units of measurement, Table 1 is extremely crucial. The goal of this meticulous 

paperwork was to ensure the research's consistency and clarity, which would reinforce the approach's integrity 

and transparency. 

Theoretical Framework 

The LCF is a dependent variable that is employed to capture the relevant elements for ecosystem condition in the 

quickly growing G-7 areas.  The LCF first came up in the literature by Siche et al. (2010), and Pata (2021) was 

the first to do empirical research on the factors that influence the LCF. The LCC theory is centered on the LCF 

indicator, which considers opportunities for ecological provision and manmade environmental pressures into 

account (Pata et al., 2023). Since the LCF includes both EFP and biocapacity in the denominator, a greater LCF 

is suggestive of a healthier environment (Pata and Kartal, 2024). The LCF offers a more thorough analysis of the 

environment by contrasting ecological footprint and biological resources (Dogan & Pata, 2022; Islam et al.,2024). 

To improve the understanding of the foregoing study, we have created the following equation (1) for LCC theory: 

𝐿𝑜𝑎𝑑 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝐹𝑎𝑐𝑡𝑜𝑟 = 𝑓(𝐺𝐷𝑃, 𝐺𝐷𝑃2, 𝐾𝑡)                      (1) 



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In equation (1), the variables for economic growth are GDP and 𝐺𝐷𝑃2 , whereas the variable for other factors 

influencing the load capacity factor is 𝐾𝑡 . Equation (2) seeks to provide an expanded view of the elements 

changing the LCF by including additional relevant variables such as globalization, urbanization, financial 

accessibility, innovation in AI, and economic growth. 

𝐿𝐶𝐹 = 𝑓(𝐺𝐷𝑃, 𝐺𝐷𝑃2, 𝐴𝐼, 𝐹𝐴, 𝐺𝑂𝐵, 𝑈𝑅𝐵𝐴)                                 (2)  

Table 1. Data and Variables 

Variables Description Logarithmic Form Unit of Measurement Source 

LCF Load Capacity 

Factor 

LLCF Gha per person GFN 

GDP Gross Domestic 

Product 

LGDP Current US$ WDI 

GDP2 Gross Domestic 

Product Square 

LGDP2 Current US$ WDI 

AI Artificial 

Intelligence 

Innovation   

LAI Patent Application in 

AI field  

Our World in Data 

FA Financial 

Accessibility 

LFA Automated teller 

machines (ATMs) 

(per 100,000 adults) 

Global Financial 

Inclusion  

GOB Globalization LGOB Globalization Index KOF Globalization 

index 

URBA Urbanization LURBA Urban Population (% 

of total population) 

WDI 

 

The load capacity factor (LCF), economic growth (GDP), artificial intelligence (AI) innovation, financial 

accessibility (FA), globalization (GOB), and urbanization (URBA) are the abbreviations used in equation (2). The 

economic explanation of this equation can be obtained by equation (3). 

𝐿𝐶𝐹𝑖𝑡 = 𝜕0 + 𝜕1𝐺𝐷𝑃𝑖𝑡 + 𝜕2𝐺𝐷𝑃𝑖𝑡
2 + 𝜕3𝐴𝐼𝑖𝑡 + 𝜕4𝐹𝐴𝑖𝑡 + 𝜕5𝐺𝑂𝐵𝑖𝑡 + 𝜕6𝑈𝑅𝐵𝐴𝑖𝑡    (3) 

In equation (4), the variables' logarithmic values are demonstrated. It increases understanding and enables the 

formulation of implications based on statistics by breaking down complex intersections into simpler linear forms. 

The logarithmic scale allows for data of different dimensions and aids in alleviating heteroscedasticity when 

broad ranges need to be reduced.  

𝐿𝐿𝐶𝐹𝑖𝑡 = 𝜕0 + 𝜕1𝐿𝐺𝐷𝑃𝑖𝑡 + 𝜕2𝐿𝐺𝐷𝑃𝑖𝑡
2 + 𝜕3𝐿𝐴𝐼𝑖𝑡 + 𝜕4𝐿𝐹𝐴𝑖𝑡 + 𝜕5𝐿𝐺𝑂𝐵𝑖𝑡 + 𝜕6𝐿𝑈𝑅𝐵𝐴𝑖𝑡    (4) 

Econometric Framework 

The present research uses the Pesaran test to assess cross-sectional connections among economies. Then, to 

guarantee data stationarity, it utilizes both second-generation tests like CIPS and CADF and first-generation tests 

like Levin, Lin & Chu (LLC) and IPS. Then, Pedroni panel cointegration tests are applied in the study to verify 

long-term connections. With the use of ARDL and quantile regression tests, it further investigates both short- and 

long-term links. Next, the DKSE, AMG, and CCEMG approaches are adopted to validate the long-run estimates' 



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robustness. With the goal to check out the causative association among the variables, the D-H causalty analysis 

is finally executed.  

 

Cross-Sectional Dependency Test 

As economies grow increasingly integrated and dependent on one another, industrialization is making CSD 

greater a problem in panel data (De Hoyos and Sarafidis, 2006). Moreover, Tufail et al. (2022) suggest that as a 

consequence of variables including reduced obstacles to trade, improved socioeconomic connectivity, the usage 

of CSD in panel data econometrics is growing. The authors of this research utilize the Pesaran's (2015) analysis 

for weakly exogenous CSD in large panel data econometrics to find whether CSD exists.  

𝐶𝑆𝐷 = √
2𝑇

𝑁(𝑁−1)𝑁
(∑ ∑ 𝐶𝑜𝑟𝑟𝑖,𝑡

̂𝑁
𝑚=𝑖+1

𝑁−1
𝑖=1 )……………………… (5) 

Panel Unit root Test 

To explore if stationarity existed in our panel data, our research investigated unit root test techniques from both 

the first and second generations. We used the Im et al. (2003)-introduced IPS test and the Levin, Lin, and Chu 

(LLC) test, which is a first-generation unit root examination invented by Levin et al. (2002). In contrast, second-

generation unit root assessments that account for slope fluctuation and CSD include CIPS and CADF, which were 

developed by Pesaran (2007). The CIPS test is the extension of the IPS examination (Polcyn et al.,2023). Voumik 

and Sultana (2022) claim that the unit-root series forms the basis for the theory. Before estimating the parameter, 

the test also recommends doing a cointegration test when the variable reaches first-difference stationarity. The 

below formula can be applied to represent the IPS test: 

∆𝑦𝑖𝑡 = 𝛿𝑖 + 𝛼𝑖𝑡 + 𝛽𝑦𝑖𝑡−1 + 𝜌𝑖∆𝑦𝑖𝑡−1 + 휀𝑖𝑡…………………………… (6) 

The LLC test statistics is given below: 

 

                     ∆yit = 𝛿𝑖yit−1 + ∑ dij∆yit−1 + Xit
′ η

ρi
j=1 + μit ……………………….(7)                         

                                       

Here,  𝑋𝑖𝑡
′   means the column vector of the independent variable and in regression 𝜂  indicates the vector of 

parameters. The CIPS unit root test is a modified version of the IPS method that looks at unit roots in individual 

time series. Within the academic community, CIPS is becoming increasingly popular because of its 

efficaciousness in handling CSD and heterogeneity. This test equation takes the following form: 

𝐶𝐼𝑃𝑆 =
1

𝑁
∑ 𝑡𝑖

𝑁
𝑖=1 (𝑁, 𝑇)………………………………………. (8) 

The CADF method is adopted to gather the statistics required by CIPS. The following equation describes the 

CADF statistics. 

∆𝑌𝑖𝑡 =  𝛽𝑖 +  𝜌𝑖𝑌𝑖,𝑡−1 + 𝜗𝑖�̅�𝑡−1 +  ∑ 𝛾𝑖𝑗∆𝑌𝑖,𝑡−1 +  휀𝑖𝑡
𝑝
𝑗=1 .................... (9) 



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Where, �̅�𝑡−1  and  ∆𝑌𝑖,𝑡−1 are average for lagged and first difference of each cross-sectional series. 

Panel Cointegration Test 

According to Anser et al. (2024), cointegration denotes a reliable, long-term link between the panel's variables. 

The Pedroni (1999) panel cointegration assessment is employed to figure out if cointegration prevails, assuming 

panel heterogeneity. This approach was adopted in the study; in contrast to Kao (1999), it permits the AR 

coefficients to vary between panels. Two separate tests were developed by Pedroni (1999, 2004). Four statistical 

measures are applied in the initial test, which uses a within-dimension approach: panel v-statistics, panel rho-

statistics, panel PP-statistics, and panel ADF-statistics. The following analysis utilizes a between-dimension 

methodology using group rho-statistics, group PP-statistics, and group ADF-statistics as its three statistical 

measures. It is carried out in this manner: 

 

𝑠ℎ𝑖𝑡 = 𝜗0 +  𝜇𝑖𝑡 + 𝛿1𝑖𝑏𝑘𝑑𝑖𝑡 + 𝛽2𝑖𝑖𝑏𝑖𝑡 + 𝜌3𝑖𝑖ℎ𝑟𝑖𝑡 + 휀𝑖𝑡…………… (10) 

Where, i=1... N for each firm in the panel and t=1,..., T denotes the time period. The estimated residuals reveal 

how far the long-run association deviates from expectations. 

Panel ARDL Model 

Using the panel ARDL framework, Pesaran et al. (1999) established the pooled mean group (PMG) technique. 

Pesaran et al. (1999) propose that the inconsistency situation, common technological advances, or the 

development of institutions that each group encountered constitute a few explanations for the homogeneity in the 

long-term connection. Additionally, by considering lag duration for both exogenous and endogenous variables, 

the ARDL model (Attiaoui and Boufateh,2019). Furthermore, this approach has the advantage of effectively 

managing autocorrelation, heteroscedasticity, and multicollinearity difficulties in models, as illustrated by Wang 

et al. (2021). In this study, the short- and long-term effects of GDP development, artificial intelligence (AI) 

innovation, financial accessibility, globalization, and urbanization on LCF were investigated using the PMG-

ARDL model. The PMG estimator relies on the ARDL model and assumes that the panel as a whole has the same 

long-run coefficients, whereas each group has unique short-term coefficients, intercepts, and error parameters.  

The ARDL simulation, which is considered relevant in this context if it can be specified as an error correction 

model when the underlying variables are somewhat integrated (I (0) and I (1)), with the restriction that the 

dependent variable is limited to just I (1) (Voumik et al.,2023b; Ridwan, 2023). Nevertheless, this method is not 

applicable when variables are integrated for order 2 removes endogeneity issues and provides reliable and 

effective estimators (Pesaran et al.,1996, 2001). The long-term association models for PMG are presented below: 

∆Y1,it = ϑ1i + β1iY1,it−1 + ∑ β1iX1,it−1 + ∑ λ1ij∆Y1,it−j + ∑ ∑ λlijΔX1,it−j + ε1i,t
k
l=2

q−1
j=0

p−1
j=1

k
l=2                                   

(11) 

Here, 𝑌𝑖 refers the dependent variable and 𝑋𝑖 are independent factors where l=1,2, 3,4. 휀𝑖𝑡  and Δ are residual & 

first difference operator accordingly.  

We found the following long-term ARDL simulation for LCF as the dependent variable: 

 

∆𝐿𝐿𝐶𝐹𝑖𝑡 =  𝜗1𝑖 + 𝛽1𝑖𝐿𝐿𝐶𝐹𝑖,𝑡−1 + 𝛽2𝑖𝐿𝐺𝐷𝑃𝑖,𝑡−1 + 𝛽3𝑖𝐿𝐺𝐷𝑃𝑖,𝑡−1
2 + 𝛽4𝑖𝐿𝐴𝐼𝑖,𝑡−1 + 𝛽5𝑖𝐿𝐹𝐴𝑖,𝑡−1 +

𝛽6𝑖𝐿𝐺𝑂𝐵𝑖,𝑡−1 +𝛽7𝑖𝐿𝑈𝑅𝐵𝐴𝑖,𝑡−1 + ∑ 𝜆1𝑖∆𝐿𝐶𝐹𝑖,𝑡−𝑚 + ∑ 𝜆2𝑖∆𝐿𝐺𝐷𝑃𝑖,𝑡−𝑚 +
𝑝
𝑖=0

𝑞
𝑚=1



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∑ 𝜆3𝑖∆𝐿𝐺𝐷𝑃𝑖,𝑡−𝑚
2 + ∑ 𝜆4𝑖∆𝐿𝐴𝐼𝑖,𝑡−𝑚 + ∑ 𝜆5𝑖∆𝐿𝐹𝐴𝑖,𝑡−𝑚 + ∑ 𝜆6𝑖∆𝐿𝐺𝑂𝐵𝑖,𝑡−𝑚 +

𝑝
𝑖=0

𝑝
𝑖=0

𝑝
𝑖=0

𝑝
𝑖=0

 ∑ 𝜆7𝑖∆𝐿𝑈𝑅𝐵𝐴𝑖,𝑡−𝑚 +  휀1𝑖,𝑡 
𝑝
𝑖=0      (12) 

The ECT and short-term correlations are examined using the Engle and Granger (1987) ECM model after long-

term relationships have been established (Voumik et al.,2023c). Equation (14) utilizes the ARDL estimate with 

error correction representation to explain the short-term link across the variables: 

 

ΔLLCFit =  ∑ α1im∆LLCFi,t−m + ∑ α2im∆LGDPi,t−m + ∑ α3im∆𝐿𝐺𝐷𝑃𝑖,𝑡−𝑚
2 + ∑ α4im∆LAIi,t−m +

p−1
i=0

p−1
i=0

p−1
i=0

q−1
m=1

∑ α5im∆LFAi,t−m + ∑ α6im∆LGOBi,t−m + ∑ α7im∆LURBAi,t−m + μ1iECT1,it−1 + ε1i,t
p−1
i=0

p−1
i=0

p−1
i=0                                      

(13)  

Quantile Regression 

Additionally, this research adopted quantile panel regression analysis for several reasons. Quantile analysis 

delivers a major benefit over conventional regression approaches as it enables one to figure out regression 

conditional quantiles and forecast how specific points within the conditional distribution will develop (Alharthi 

et al., 2021). The study employed the panel quantile regression model proposed by Koenker and Bassett (1978) 

as an illustration as it permits users to utilize the values of the explanatory factors to judge the change in the 

dependent variable and the conditional mean (Masiero et al., 2015). This offers vital details on the links between 

variables in numerous situations or quantiles (Kilinc-Ata et al.,2024). Academics from a wide range of subjects, 

such as environmental research (Carfora et al., 2017), economics (Shahzad et al., 2017), and clinical studies 

(Olsen et al., 2017), are embracing QR widely due to its numerous advantages. The QR can be shown by the 

following equation- 

   Q𝐿𝑅𝐶𝑌it  (∂ |∅0, xit, μi) = ∅0  + μit + ∅1 ∂LGDPit + ∅2 ∂𝐿𝐺𝐷𝑃2
it + ∅3 ∂𝐿𝐴𝐼it + ∅4 ∂LFAit  + ∅5 ∂LGOBit  +

  ∅6 ∂LURBAit++  εit …………… (14)  

Here,(∂ |∅0, xit, μi) is the ∂ th conditional quantile. Moreover, the notion ∂ and Xit indicates the quantile measure 

and independent factors accordingly. 

Robustness Check 

This stage involves running the DKSE, AMG, and CCEMG procedures to confirm the results' robustness. To 

obtain the values of explanatory variables, we implemented three distinct estimators to find the long-

run hyperlink. The average values of the findings for the explanatory variable are used in addition to the residuals 

in the Driscoll and Kraay (1998) Standard Error. When there is cross-sectional reliance, Driscoll-Kraay standard 

errors are employed because they are heteroscedastic, autocorrelation consistent, as well as resistant to typical 

forms of cross-sectional and temporal dependency (Hoechle, 2007). Teal and Eberhardt (2010) included the 

production function in their revised augmented mean group (AMG) panel estimator. The primary advantage of 

this method is that it can aid in the correction of outcomes when panel heterogeneity and multifaceted error terms 

are present (Nathaniel & Iheonu, 2019). In conclusion, Pesaran (2006) developed this estimate model to replace 

the CCEMG estimator. This method generates reliable figures, allows time-varying unobserved factors with 

varying influences across panel members and robust against CSD problem. This approach can handle both an 

infinite number of "weak" factors and a finite number of "strong" unobserved common elements (Anshasy and 

Katsaiti, 2014; Addae et al.,2023). 

 



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D-H casuality Test 

Causality tests are required to identify the relevant policy implications for managing the emergence of the LCF. 

A technique for evaluating causal linkages between the components was presented by Granger (1969); however, 

it has drawbacks and cannot be applied when panel data has a CSD problem. The cointegration connection 

suggests all factors are in a long-term equilibrium. Thus, we use the pane causality test of Dumitrescu et al. (2021) 

to examine their causative relationship. Because it can determine both N > T and T > N samples, this strategy can 

be flexible and beneficial for getting precise outcomes throughout CD (Ahmed and Le, 2021). By comparing each 

of the N factors to a minimum of one causal link in the panel, this method evaluates the null hypothesis of non-

causality in each instance. In particular, the alternative hypothesis claims that at least a causality might be 

discovered in the panel (Hurtado et al., 2024). 

Result and Discussion 

Table 1 illustrates the statistical outcome of several measures of normality, such as skewness, probability, 

kurtosis, and the Jarque-Bera test. The dataset covers the G-7 nations from 1990 to 2019 and contains 91 

observations for each variable. Using the following descriptive statistics, the seven variables (LLCF, LGDP, 

LGDPSQ, LAI, LGOB, LFA, and LURBA) are characterized.  

 

Table 2. Summary Statistics 

Statistic LLCF LGDP LGDP2 LAI LFA LGOB LURBA 

Mean -0.943801 10.69067 114.3221 5.522956 4.873562 4.428334 4.387441 

Median -1.121269 10.67368 113.9275 5.513429 4.803375 4.426225 4.397432 

Maximum 0.723357 11.24282 126.4009 9.709417 5.907974 4.493778 4.521299 

Minimum -2.038284 10.317 106.4405 1.609438 4.375432 4.292134 4.224305 

Std. Dev. 0.779588 0.178842 3.840701 2.003691 0.373498 0.053247 0.075888 

Skewness 0.811059 0.519595 0.575074 0.220978 0.879703 -0.72077 -0.260761 

Kurtosis 2.868482 3.496751 3.583357 2.426018 2.993025 2.734066 3.070021 

Jarque-Bera 10.04246 5.030319 6.306099 1.989793 11.73734 8.147367 1.049867 

Probability 0.006596 0.08085 0.042722 0.369762 0.002827 0.017015 0.591595 

Sum -85.88593 972.851 10403.31 502.589 443.4941 402.9784 399.2571 

Sum Sq. Dev. 54.69818 2.878612 1327.589 361.3301 12.55509 0.255174 0.51831 

Observations 91 91 91 91 91 91 91 

 

All selected variables have positive means, except for LLCF, as can be observed in the table. Additionally, most 

of the variables' estimated standard deviations are quite small, suggesting that the data points are a little temporally 

changeable and concentrated around the mean. The data for each variable, including the mean, standard deviation, 

lowest and maximum values, and the number of observations, will be shown in the box below. Other than LGOB 

and LLCF, which have a negative skew, the majority of the variables have a positive skew. Furthermore, each 

variable in this research was verified to have a normal distribution using the Jarque-Bera normality test. Given 

that it takes into account both skewness and any anomalous kurtosis, this test is suitable. 

The Pesaran (2004) CSD evaluation findings are displayed in Table 03 below. The p-values presented above 

conclusively indicate that at 1% significance levels, all of the variables (LLCF, LGDP, LGDPSQ, LAI, LFA, 



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LGOB, and LURBA) have statistical significance. In our research, cross-sectional dependency is accepted as the 

alternate hypothesis of the CD test. This suggests that our data collection contains CSD. 

 

Table 3. Results of CSD test 

Variables  CD-Statistics P-Value 

LLCF 5.84*** 0.000 

LGDP 5.36*** 0.000 

LGDP2 5.37*** 0.000 

LAI 13.33*** 0.000 

LFA 3.96*** 0.003 

LGOB 11.46*** 0.000 

LURBA 16.61*** 0.000 

 

Before doing a cointegration inquiry, it is essential to conduct extensive unit root testing to see whether the 

variables are stationary. The outcomes of these unit root analyses are displayed in Table 04. In this research, LLC, 

and IPS, first-generation unit root tests were utilized in conjunction with CIPS and CADF, second-generation 

tests. The variables LAI, LFA, and LGOB are the only variables that show stationary behavior at the first 

difference, based on the LLC test findings. At the I(1) difference, the other variables likewise exhibit stationarity. 

At the 1% significance thresholds, each of these factors is significant. However, the findings of the IPS test 

indicate that only LFA and LGOB stay stable at their initial level all other variables (LLCF, LGDP, LGDPSQ, 

LAI, and LURBA) are similarly significant at the 1% significance level and are stationary at the first difference 

(I(1)). 

The stationarity characteristics of the variables were further investigated utilizing the CIPS and CADF tests to 

guarantee more dependable findings. By adding cross-sectional averages of lag values and initial differences, 

these tests extend the capabilities of first-generation tests.   Moreover, Table 05 demonstrates that, with the 

exception of LAI and LGOB, all variables are stationary following the first difference based on the CIPS test. In 

contrast, the findings of the CADF test indicate that all other variables are stationary at the first difference I(1), 

except for the LAI and LFA, which are stationary at their level form. The outcome of this investigation indicates 

that the components have undergone considerable cointegration, hence removing the possibility of a unit root 

issue. 

Table 4. Panel Unit Root Test 

Variables Levin, Lin &Chu IPS CIPS CADF Decision 

I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1) 

LLCF -2.496 -5.071*** -0.702 -3.754*** -0.629 -3.869*** -1.230 -3.651*** I(1) 

LGDP -0.843 -5.947*** -1.622 -4.604*** -1.835 -3.392*** -1.469 -3.722*** I(1) 

LGDP2 -0.657 -5.957*** -1.614 -4.600*** -1.882 -3.498** -1.516 -3.584*** I(1) 

LAI -5.346*** -5.705*** -1.229 -4.262*** -2.895** -4.634*** -3.023** -3.838*** I(0) 

LFA -5.687*** -6.087*** -3.255** -4.740*** -1.098 -3.562*** -2.985** -3.812*** I(0) 

LGOB -5.109*** -4.415*** -3.888*** -4.436*** -3.098** -5.462*** -1.530 -3.480*** I(0) 

LURBA -0.132 -4.267*** -2.599 -7.924*** -0.659 -3.859*** -0.882 -3.087** I(1) 

 



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Table 05 illustrates the Pedroni panel cointegration test findings, encompassing both within- and between-

dimension investigation. There is no indication of cointegration because the p-values for the Panel v-Statistic and 

Panel rho-Statistic (0.5186 and 0.9987, respectively) are higher than the conventional significance threshold. 

Nonetheless, the Panel PP-Statistic and Panel ADF-Statistic p-values are less than the traditional significance 

criteria, indicating that the null hypothesis of no cointegration is rejected. Cointegration appears to be present 

based on these figures. The Group rho-statistic in the between-dimension analysis displays a very high p-value of 

0.9381, suggesting that there is not enough evidence for cointegration in any of the panels. The Group ADF-

Statistic and the Group PP-Statistic, on the other hand, both reveal evidence of cointegration across the panels, 

with p-values of 0.000. Consequently, all of the variables are cointegrated over the long run, based on Pedroni's 

cointegration techniques. 

Table 5. Panel Cointegration Test 

Alternative hypothesis: common AR coefs. (within-dimension) 

 Statistic Prob. 
Weighted 

Statistics 
Prob. 

Panel v-Statistic 1.30446 0.0960 -0.04653 0.5186 

Panel rho-Statistic 2.79995 0.9974 3.00827 0.9987 

Panel PP-Statistic -2.50037 0.0000 -2.08538 0.0000 

Panel ADF-Statistic -3.82612 0.0000 -4.08531 0.0000 

Alternative hypothesis: individual AR coefs. (between-dimension) 

  Statistic Prob.   
Group rho-Statistic 4.47852 0.9381   
Group PP-Statistic -2.40568 0.0000   
Group ADF-Statistic -4.01587 0.0000   

 

The conclusions obtained from the Panel ARDL model, as illustrated in Table 06, shed light on the intricate 

dynamics affecting LCF in the G-7 nations. For LGDP as a starting point, the long-run coefficient is -0.048, 

statistically significant at traditional levels. GDP has a negative coefficient of -0.045 in the short term, which is 

statistically insignificant with a p-value over the usual level. It demonstrates that GDP has a considerable 

implication on LCF in the chosen area, suggesting that in this particular context, economic growth alone may be 

a major contributor to environmental degradation. According to Ang (2007) and Raihan et al.(2023a) economic 

expansion contributes to environmental degradation and has an advantageous effect on the emissions of CO2. It 

was discovered that economic expansion posed a challenge to the reduction in emissions (Liu et al.,2020; Liu et 

al.,2016; Chen et al.,2022; Raihan et al.,2022b; Pattak et al.,2023; Voumik et al.,2023a; Raihan et al.,2023c; 

Ridwan et al.,2023; Ridwan et al.,2024a; Raihan et al.,2024c; Raihan et al.,2024d). Furthermore, Arouri et al. 

(2012) added that the main factor contributing to environmental deterioration in MENA nations is GDP growth. 

However, Acheampong et al. (2022) observed that Australia's CO2 emissions are not heavily impacted by 

fluctuations in GDP.  

In the short and long terms, there is an encouraging link between LGDP2 and LCF, with statistically significant 

coefficients. These results demonstrate that rising GDP has a beneficial long-term impact on the environmental 

conditions in the G-7 economies. The study shows that, in both cases, Artificial Intelligence (LAI) and LCF have 

a substantial positive link. Over the short and long terms, an extra one percent in LAI causes an equivalent rise in 

LCF of 0.029% and 0.142%, respectively. These findings highlight the need for AI innovation to guarantee the 



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G-7 region's ecological viability over time. In a similar vein, LFA and LCF have a positive correlation over the 

short and long terms, suggesting that having access to money can have a good environmental impact. With p-

values less than 0.05 in each scenario, these results are statistically significant. This might be because immediate 

environmental surveillance strengthens the utilization of resources, and the application of AI can boost energy 

conservation in numerous areas, including residential electricity usage, travel, and manufacturing.. Moreover, 

novel technologies should be encouraged by policymakers to safeguard the environment and advance biodiversity 

(Alavijeh et al.,2023). The table indicates that there is a negative correlation between globalization (LGOB) and 

LCF in both the short and long term; however, the effect is statistically significant in the long term and negligible 

in the short term. This shows that while globalization promotes increased commerce and energy demand, it is not 

potentially good for biodiversity. The growing need for commodities and amenities across national borders made 

accessible by global commerce has led to a spike in greenhouse gas emissions due to globalization (Kirikkaleli et 

al.,2023). Study by Shahbaz et al.(2018) in Japan and Sharif et al.(2022) in G-& area also discovered that 

globalization is harmful for the ecosystem. However, globalization aids in resolving this problem and enhancing 

the environment (Adebayo et al., 2022). Furthermore, Khurshid et al. (2024) demonstrate that environmental 

sustainability is positively impacted by globalization.  

Similarly, urbanization (LURBA) has a negative connection with LCF in both short- and long-term assessments. 

Over time, a 1% spike in LURBA generates a small but statistically significant 0.044% drop in LCF at 

conventional levels. With a p-value above 0.05, the findings, however, are not significant in the short run. 

Furthermore, a significant short-term reduction of 0.222% in LCF is linked to a 1% rise in LURBA. One possible 

reason for this outcome can be the loss of forests and the destruction of natural environments for construction are 

common consequences of growth in urban areas, which decrease diversity and disturb ecology. This conclusion 

defies those of Aye et al. (2017), who claimed that economic expansion tends to cut CO2 emissions in low-growth 

regimes and spikes in high-development regimes.  

We used the quantile regression (QR) approach to explore the factors influencing the LCF in the G-7 economies. 

Five quantile points the fifth, 25th, 50th, 75th, and 95th were chosen especially for this study's regression 

calculation. First, for all quantiles, there is a negative correlation between the variable LGDP and LLCF. 

This finding suggests that in the chosen area, economic expansion had a positive impact on environmental 

standards. In the first and fifth quantiles, the coefficient is significant at the 1% significance thresholds; in the 

remaining quantiles, it is significant at the 5% significance level. These indicate that GDP might have a 

detrimental effect on the environment of G-7 region. 

Second, across all quantiles, LGDP2 has an upward association with LCF which is statistically significant at 

different levels. The first quantile has the lowest coefficient value, while the second quantile has the greatest. This 

investigation has taken into account LAI, the third important component impacting LCF. Except for the fourth 

quantile, which is significant at the 5% level, the outcomes illustrated that the LAI coefficients are positively 

related to LCF and statistically significant at the 1% level for all quantiles. Interestingly, the second quantile has 

the most effect; at the 25th percentile, an additional 1% in AI corresponds to a 0.094% increase in LCF. This 

indicates that adoption of AI is beneficial for G-7 area. 

The findings show an upward trend between the LFA variable and LCF, which is significant at the 1% level for 

all quantiles. A 1% rise in LFA causes an LLCF increase of 0.84% in the first quantile and 2.11% in the final 

quantile. Stated differently, financial accessibility in the G-7 economies fosters circularity and improves 

ecological conditions. It is noteworthy that there is a considerable rise in impact intensity throughout the 

quantiles. Likewise, there is a beneficial relationship between the dependent variable, LLCF and the variable 

LGOB. The intensity rises through the upper quantiles, reaching 11.18 in the fifth quantile, with the exception of 

the second quantile, where the coefficient value is 2.775.  



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Table 6. Panel ARDL Model 

Long-run Estimation 

Variable Coefficient Std. Error t-Stat p-Value 

LGDP -0.048 0.0298 -2.8743 0.0234 

LGDP2 0.069 0.0689 2.8604 0.0471 

LAI 0.142 0.0930 1.5359 0.0034 

LFA 0.793 0.0574 2.3248 0.0258 

LGOB -1.822 0.1019 -2.3949 0.0221 

LURBA -0.044 0.0218 -2.1418 0.0393 

Short-run Estimation 

Variable Coefficient Std.Error t-stat P-value 

COINTEQ01 -0.039 0.1887 -1.207399 0.0369 

D(LGDP) -0.045 0.0046 -2.403889 0.0689 

D(LGDP2) 0.121 0.0571 1.393902 0.0219 

D(LAI) 0.029 0.0595 2.182761 0.0057 

D(LFA) 0.088 0.9665 0.091868 0.0273 

D(LGOB) -0.671 1.2798 -0.857848 0.0967 

D(LURBA) -0.222 0.4836 -0.934931 0.0561 

 

Finally, with a single instance of the last quantile, the coefficient values for the indicator LURBA are negative 

for every quantile. The discoveries demonstrate statistical significance at the 1% level in the first, second, and 

fifth quantiles; however, the finding is not significant in the third and fourth quantiles. It is clear from observation 

that the final quantile has the biggest coefficient value (3.66). The outcome suggests that urbanization in the 

chosen area has greater adverse impacts on the natural landscape.  

A number of estimating techniques, including DKSE, AMG, and CCEMG, were used to further examine the 

accuracy of quantile regression estimates and ARDL findings. Table 08 contains information on the DKSE, AMG, 

and CCEMG results. The projected LGDP values for the three tests are, respectively, -0.123, -0.049, and -0.054. 

These results indicate that economic expansion advantages the environment quality in the G-7 countries. The 

DKSE and CCEMG estimators have a significant coefficient value at 1%, but the AMG estimator has a significant 

coefficient value at the 5% level. The outcome is aligns with the panel ARDL model and quantile regression 

conclusions. 

Conversely, the LCF variable has a positive correlation with LGDP2, LAI, and LFA. The LGDP2 coefficient 

values are significant at the 10% level in the AMG test and identical at the 1% significance level in the DKSE 

and CCEMG tests. In particular, a boost of 1% in AI innovation drives LCF to go up by 0.0077%, 0.0466%, and 

0.0129%, in that sequence. This suggests that the G-7 countries' ecosystems may be improved by implementing 

AI technology. These results align with inferences made from the Panel ARDL and QR estimations. On the other 

hand, the LCF variable reveals adverse associations for LGOB and LURBA, suggesting that growing urbanization 

and globalization are detrimental to biodiversity in the chosen places. At the 1% level in the DKSE estimation, 

the 10% level in the AMG estimation, and the 5% level in the CCEMG estimation, LGOB and LURBA are both 

statistically significant. The LGOB result contradicts the conclusions of the ARDL model and only agrees with 

the QR results. In the meanwhile, the Panel ARDL and QR findings agree with the LURBA result. Thus QR and 

ARDL model, which serve as the main estimated method in this work, are validated by the outcomes obtained.  

 



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Table 7. Quantile regression approach 

 (1) (2) (3) (4) (5) 

VARIABLES Q0.05 Q0.25 Q0.50 Q0.75 Q0.95 

      

LGDP -0.683*** -0.072** -0.282** -0.031** -0.039*** 

 (0.0718) (0.6018) (0.8215) (0.2439) (0.0845) 

LGDP2 0.017*** 0.499** 0.226** 0.106** 0.178 

 (0.0268) (0.0115) (0.0971) (0.9761) (0.0799) 

LAI 0.047*** 0.094*** 0.020*** 0.019** 0.089*** 

 (0.0086) (0.0325) (0.0675) (0.0636) (0.0257) 

LFA 0.846*** 0.648*** 2.184*** 2.282*** 2.111*** 

 (0.0424) (0.160) (0.332) (0.313) (0.127) 

LGOB 2.902*** 2.775*** 7.968*** 10.27*** 11.18*** 

 (0.233) (0.879) (1.824) (1.719) (0.695) 

LURBA -2.219*** -3.871*** -1.945 -0.0655 3.666*** 

 (0.208) (0.786) (1.632) (1.538) (0.622) 

Constant -45.419*** -92.712** -29.123*** -15.765** -52.836 

 (10.822) (20.723) (13.712) (7.323) (29.081) 

      

Observations 91 91 91 91 91 

      

Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 

The findings of the D-H causality assessment for the LCF of the G-7 economy are summarized in Table 9. The 

null hypothesis, which claims that the factor under investigation does not consistently cause another variable, can 

be rejected if the p-value approaches significant values of 1%, 5%, or 10%. Based on the research, the p-value of 

0.0039 demonstrates that the effect of LGDP on LLCF is statistically significant at the 1% level. As a result, the 

null hypothesis can be rejected, guiding us to the conclusion that there is only one way of causality from LGDP 

to LLCF. Conversely, as the p-value is higher than the expected values, there is not a significant correlation 

between LLCF and LGDP. This result suggests a one-way connection and illustrates how ecological health in the 

G-7 countries can be affected by economic expansion. 

Table 8. Robustness check 

 (1) (2) (3) 

VARIABLES DKSE AMG CCEMG 

    

LGDP -0.123*** -0.049** -0.054*** 

 (0.0090) (0.0264) (0.0813) 

LGDP2 0.021*** 0.068* 0.199*** 

 (0.0102) (0.1210) (0.0531) 

LAI 0.00770*** 0.0466*** 0.0129*** 

 (0.0215) (0.0656) (0.223) 

LFA 0.284*** 0.493** 0.685** 

 (0.009) (0.0464) (3.927) 

LGOB -0.912*** -0.546* 0.420** 

 (0.0532) (0.799) (0.267) 

LURBA -0.502*** -0..971* -0.552** 

 (1.294) (11.74) (45.70) 

Constant 20.218** 17.604** 12.981** 

 (0.2671) (0.5019) (0.8765) 

    

Observations 91 91 91 

Number of groups 7 7 7 

Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 



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Table 9. D-H Casuality Test 

Null Hypothesis Observation Prob. 

LGDP ≠ LLCF 91 0.0039 

LLCF  ≠ LGDP  0.1135 

LGDP2 ≠  LLCF 91 0.0062 

LLCF  ≠ LGDP2  0.1163 

LAI ≠  LLCF 91 0.0208 

LLCF ≠ LAI  0.0409 

LFA  ≠ LLCF 91 0.0527 

LLCF  ≠ LFA  0.5953 

LGOB ≠  LLCF 91 0.0148 

LLCF  ≠ LGOB  0.9493 

LURBA ≠  LLCF 91 0.0609 

LLCF  ≠ LURBA  0.5379 

 

Further examination exposes a similar unidirectional link between LLCF and LGDP2. Furthermore, as we are 

unable to exclude the null hypothesis in this instance, the results indicate fluctuations in LLCF have no effect on 

LGDP2. The LCF and LAI, on the other hand, have a bidirectional hyperlink, evidenced by statistically significant 

p-values across all studies. Furthermore, as the suggested p-values are greater than traditional levels, there prevails 

no association from LFA to LLCF, LCF to LFA, LURBA to LLCF and LLCF to LURBA. Nevertheless, there 

exists a substantial unidirectional causal connection between LGOB and LLCF as the p-value is 0.0148 and it is 

significant at 1% significance level. This outcome reveals that we can reject the null hypothesis and come to the 

conclusion that globalization degrade the ecosystem. 

 

Conclusion  

This comprehensive study discusses the complex relationships among globalization, urbanization, financial 

accessibility, AI innovation, economic growth, and LCF in the G-7 territory between 1990 and 2019. The LCC 

hypothesis is used in this research to determine how such significant variables affect LCF dynamics. Data 

stationarity was observed using both first and second-generation unit root examinations to guarantee accurate 

estimation and to establish that the dataset was free of unit root problems. The panel cointegration test was also 

utilized in the inquiry to clarify the heterogeneous coefficients and demonstrate long-term cointegration across 

the factors under consideration.  The Panel Autoregressive Distributive Lag Model (ARDL) in conjunction with 

quantile regression techniques enables an expanded examination of the complicated interactions between the 

dependent and explanatory variables. The results underscore the vital role of urbanization, GDP, and globalization 

in the higher rates of environmental pollution in the G-7 region. However, the quality of the ecosystem benefits 

from financial accessibility and AI innovation. To guarantee the correctness of the analytical framework, rigorous 

steps including DKSE, AMG, and CCEMG were implemented. The robustness of the quantile regression and 

panel ARDL studies was confirmed by all these techniques. Furthermore, Dumitrescu and Hurlin (D-H) causality 

tests were used to explore the causal links between each variable. The findings demonstrated a unidirectional 

causal association between LLCF and LGDP, LAI, and LGOB. In addition, it was discovered that none of the 

three factors financial accessibility, urbanization, and load capacity factor drives the other. Our investigation 

delivers fascinating insights into the complexities of changing LCF patterns in the G-7, which has major 



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implications for stakeholders and decision-makers who are committed to promoting green policies and equitable 

growth in those nations. 

Creating policies that support both environmental sustainability and financial advancement is crucial to 

addressing the G-7 region's U-shaped link between wealth and load capacity factor. To lessen the negative effects 

on environmental sustainability as wealth grows, stricter regulations on resource use and pollution must be 

implemented. Early on in the economic growth process, environmental degradation may be successfully stopped 

by offering tax breaks and subsidies to encourage sustainable practices and green technologies. Furthermore, 

funding programs for awareness and education might encourage long-term client behavior. The promotion of 

advanced clean technologies and renewable energy sources must be the focus of policy given the continuous rise 

in wealth (Raihan et al.,2024e). This will ensure that increasing income levels have more detrimental effects on 

the environment. Encouraging corporate responsibility and integrating sustainability metrics into financial 

reporting have the potential to significantly accelerate companies' transition to greener practices. The G-7 nations' 

cooperation and idea sharing can increase the effectiveness of these programs even further. The G-7 area may 

attain a healthy balance between environmental sustainability and economic growth through the implementation 

of a progressive and adaptable plan. The results draw attention to important policy implications for the G-7 

countries that want to improve environmental sustainability. Governments ought to give AI research and 

development top priority, concentrating on innovations that track, forecast, and lessen environmental effects. 

Promoting public-private partnerships may hasten the utilization of AI solutions in sectors including 

transportation, energy, and agriculture, maximizing resource efficiency and cutting emissions. Furthermore, 

financial institutions must to be encouraged to offer easily available capital for environmentally friendly solutions 

so that small and beginning businesses may support the sustainability agenda. Broader adoption can be facilitated 

by implementing tax credits, subsidies, and low-interest loans for initiatives that benefit the environment. In order 

to promote inclusive growth and fair access to sustainable technology, policymakers must also make sure that 

underprivileged people are included in the financial accessibility framework. Financial strategy integration with 

AI may improve decision-making even more, resulting in environmental policies that are more flexible and 

successful. All things considered, a cooperative strategy that blends financial accessibility with AI innovation 

may greatly advance the G-7's pursuit of sustainability and spearhead international efforts to tackle climate 

change. The outcomes presents substantial policy frameworks for tackling the decreasing load capacity factor in 

the G-7 area caused by globalization and urbanization. Policymakers must to adopt measures to effectively handle 

urban expansion in a manner that is environmentally responsible, with a particular focus on promoting the 

establishment of eco-friendly infrastructure and intelligent urban initiatives to optimize the use of resources. 

Encouraging the implementation of mixed-use projects and public transit can help mitigate the environmental 

impact of urbanization. In addition, it is imperative for governments to enforce regulations and provide incentives 

to encourage firms to embrace sustainable practices in their global supply chains, therefore reducing their impact 

on the environment. Promoting local production and consumption can mitigate the environmental consequences 

of globalization. In order to sustain the carrying capacity, it is imperative to enforce more stringent environmental 

rules and standards for both local and foreign businesses. Furthermore, allocating resources to renewable energy 

sources and energy-efficient technology might aid in reducing the negative impacts of growing urbanization and 

global economic activity. Facilitating global collaboration to exchange optimal methodologies and technology 

for promoting sustainability will be essential. In order to preserve the ecological equilibrium and assure long-term 

sustainability in the G-7 region, it is imperative to adopt a comprehensive policy strategy that tackles the 

environmental consequences of globalization and urbanization. 

 

 



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Declaration  

 

Acknowledgment: N/A 

 

Funding: N/A 

 

Conflict of interest: N/A 

 

Ethics approval/declaration: N/A 

 

Consent to participate: N/A 

 

Consent for publication: N/A 

 

Data availability: Data available on request 

 

Authors contribution: Shewly Bala led the conceptualization, data analysis, and manuscript preparation; Sarder 

Abdulla Al Shiam contributed to methodology and validation; S M Shamsul Arefeen handled statistical analysis; 

Shake Ibna Abir assisted with literature review and editing; Hemel Hossain managed data collection; Md Sibbir 

Hossain worked on coding and model implementation; Shaharina Shoha provided supervision; Afsana Akhter 

aided in data curation; Mohammad Ridwan reviewed and revised the manuscript; Sumaira supported with 

administration and formatting. 

 

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