Global Sustainability Research ISSN: 2833-986X https://doi.org/10.56556/gssr.v3i4.974 Global Scientific Research 1 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, Global Sustainability Research Global Scientific Research 2 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 Β°C and 1.5 Β°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 Global Sustainability Research Global Scientific Research 3 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; SchrΓΆder 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 Global Sustainability Research Global Scientific Research 4 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 Global Sustainability Research Global Scientific Research 5 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 Global Sustainability Research Global Scientific Research 6 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: πΏπ‘œπ‘Žπ‘‘ πΆπ‘Žπ‘π‘Žπ‘π‘–π‘‘π‘¦ πΉπ‘Žπ‘π‘‘π‘œπ‘Ÿ = 𝑓(𝐺𝐷𝑃, 𝐺𝐷𝑃2, 𝐾𝑑) (1) Global Sustainability Research Global Scientific Research 7 In equation (1), the variables for economic growth are GDP and 𝐺𝐷𝑃2 , whereas the variable for other factors influencing the load capacity factor is 𝐾𝑑 . 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. 𝐿𝐢𝐹 = 𝑓(𝐺𝐷𝑃, 𝐺𝐷𝑃2, 𝐴𝐼, 𝐹𝐴, 𝐺𝑂𝐡, π‘ˆπ‘…π΅π΄) (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). 𝐿𝐢𝐹𝑖𝑑 = πœ•0 + πœ•1𝐺𝐷𝑃𝑖𝑑 + πœ•2𝐺𝐷𝑃𝑖𝑑 2 + πœ•3𝐴𝐼𝑖𝑑 + πœ•4𝐹𝐴𝑖𝑑 + πœ•5𝐺𝑂𝐡𝑖𝑑 + πœ•6π‘ˆπ‘…π΅π΄π‘–π‘‘ (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. 𝐿𝐿𝐢𝐹𝑖𝑑 = πœ•0 + πœ•1𝐿𝐺𝐷𝑃𝑖𝑑 + πœ•2𝐿𝐺𝐷𝑃𝑖𝑑 2 + πœ•3𝐿𝐴𝐼𝑖𝑑 + πœ•4𝐿𝐹𝐴𝑖𝑑 + πœ•5𝐿𝐺𝑂𝐡𝑖𝑑 + πœ•6πΏπ‘ˆπ‘…π΅π΄π‘–π‘‘ (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' Global Sustainability Research Global Scientific Research 8 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. 𝐢𝑆𝐷 = √ 2𝑇 𝑁(π‘βˆ’1)𝑁 (βˆ‘ βˆ‘ πΆπ‘œπ‘Ÿπ‘Ÿπ‘–,𝑑 ̂𝑁 π‘š=𝑖+1 π‘βˆ’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: βˆ†π‘¦π‘–π‘‘ = 𝛿𝑖 + 𝛼𝑖𝑑 + π›½π‘¦π‘–π‘‘βˆ’1 + πœŒπ‘–βˆ†π‘¦π‘–π‘‘βˆ’1 + νœ€π‘–π‘‘β€¦β€¦β€¦β€¦β€¦β€¦β€¦β€¦β€¦β€¦β€¦ (6) The LLC test statistics is given below: βˆ†yit = 𝛿𝑖yitβˆ’1 + βˆ‘ dijβˆ†yitβˆ’1 + Xit β€² Ξ· ρi j=1 + ΞΌit ……………………….(7) Here, 𝑋𝑖𝑑 β€² means the column vector of the independent variable and in regression πœ‚ 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: 𝐢𝐼𝑃𝑆 = 1 𝑁 βˆ‘ 𝑑𝑖 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. βˆ†π‘Œπ‘–π‘‘ = 𝛽𝑖 + πœŒπ‘–π‘Œπ‘–,π‘‘βˆ’1 + πœ—π‘–οΏ½Μ…οΏ½π‘‘βˆ’1 + βˆ‘ π›Ύπ‘–π‘—βˆ†π‘Œπ‘–,π‘‘βˆ’1 + νœ€π‘–π‘‘ 𝑝 𝑗=1 .................... (9) Global Sustainability Research Global Scientific Research 9 Where, οΏ½Μ…οΏ½π‘‘βˆ’1 and βˆ†π‘Œπ‘–,π‘‘βˆ’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: π‘ β„Žπ‘–π‘‘ = πœ—0 + πœ‡π‘–π‘‘ + 𝛿1π‘–π‘π‘˜π‘‘π‘–π‘‘ + 𝛽2𝑖𝑖𝑏𝑖𝑑 + 𝜌3π‘–π‘–β„Žπ‘Ÿπ‘–π‘‘ + νœ€π‘–π‘‘β€¦β€¦β€¦β€¦β€¦ (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: βˆ†Y1,it = Ο‘1i + Ξ²1iY1,itβˆ’1 + βˆ‘ Ξ²1iX1,itβˆ’1 + βˆ‘ Ξ»1ijβˆ†Y1,itβˆ’j + βˆ‘ βˆ‘ Ξ»lijΞ”X1,itβˆ’j + Ξ΅1i,t k l=2 qβˆ’1 j=0 pβˆ’1 j=1 k l=2 (11) Here, π‘Œπ‘– refers the dependent variable and 𝑋𝑖 are independent factors where l=1,2, 3,4. νœ€π‘–π‘‘ and Ξ” are residual & first difference operator accordingly. We found the following long-term ARDL simulation for LCF as the dependent variable: βˆ†πΏπΏπΆπΉπ‘–π‘‘ = πœ—1𝑖 + 𝛽1𝑖𝐿𝐿𝐢𝐹𝑖,π‘‘βˆ’1 + 𝛽2𝑖𝐿𝐺𝐷𝑃𝑖,π‘‘βˆ’1 + 𝛽3𝑖𝐿𝐺𝐷𝑃𝑖,π‘‘βˆ’1 2 + 𝛽4𝑖𝐿𝐴𝐼𝑖,π‘‘βˆ’1 + 𝛽5𝑖𝐿𝐹𝐴𝑖,π‘‘βˆ’1 + 𝛽6𝑖𝐿𝐺𝑂𝐡𝑖,π‘‘βˆ’1 +𝛽7π‘–πΏπ‘ˆπ‘…π΅π΄π‘–,π‘‘βˆ’1 + βˆ‘ πœ†1π‘–βˆ†πΏπΆπΉπ‘–,π‘‘βˆ’π‘š + βˆ‘ πœ†2π‘–βˆ†πΏπΊπ·π‘ƒπ‘–,π‘‘βˆ’π‘š + 𝑝 𝑖=0 π‘ž π‘š=1 Global Sustainability Research Global Scientific Research 10 βˆ‘ πœ†3π‘–βˆ†πΏπΊπ·π‘ƒπ‘–,π‘‘βˆ’π‘š 2 + βˆ‘ πœ†4π‘–βˆ†πΏπ΄πΌπ‘–,π‘‘βˆ’π‘š + βˆ‘ πœ†5π‘–βˆ†πΏπΉπ΄π‘–,π‘‘βˆ’π‘š + βˆ‘ πœ†6π‘–βˆ†πΏπΊπ‘‚π΅π‘–,π‘‘βˆ’π‘š + 𝑝 𝑖=0 𝑝 𝑖=0 𝑝 𝑖=0 𝑝 𝑖=0 βˆ‘ πœ†7π‘–βˆ†πΏπ‘ˆπ‘…π΅π΄π‘–,π‘‘βˆ’π‘š + νœ€1𝑖,𝑑 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: Ξ”LLCFit = βˆ‘ Ξ±1imβˆ†LLCFi,tβˆ’m + βˆ‘ Ξ±2imβˆ†LGDPi,tβˆ’m + βˆ‘ Ξ±3imβˆ†πΏπΊπ·π‘ƒπ‘–,π‘‘βˆ’π‘š 2 + βˆ‘ Ξ±4imβˆ†LAIi,tβˆ’m + pβˆ’1 i=0 pβˆ’1 i=0 pβˆ’1 i=0 qβˆ’1 m=1 βˆ‘ Ξ±5imβˆ†LFAi,tβˆ’m + βˆ‘ Ξ±6imβˆ†LGOBi,tβˆ’m + βˆ‘ Ξ±7imβˆ†LURBAi,tβˆ’m + ΞΌ1iECT1,itβˆ’1 + Ξ΅1i,t pβˆ’1 i=0 pβˆ’1 i=0 pβˆ’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- QπΏπ‘…πΆπ‘Œit (βˆ‚ |βˆ…0, xit, ΞΌi) = βˆ…0 + ΞΌit + βˆ…1 βˆ‚LGDPit + βˆ…2 βˆ‚πΏπΊπ·π‘ƒ2 it + βˆ…3 βˆ‚πΏπ΄πΌit + βˆ…4 βˆ‚LFAit + βˆ…5 βˆ‚LGOBit + βˆ…6 βˆ‚LURBAit++ Ξ΅it …………… (14) Here,(βˆ‚ |βˆ…0, xit, ΞΌi) is the βˆ‚ th conditional quantile. Moreover, the notion βˆ‚ 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). Global Sustainability Research Global Scientific Research 11 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, Global Sustainability Research Global Scientific Research 12 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) Global Sustainability Research Global Scientific Research 13 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 Global Sustainability Research Global Scientific Research 14 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. Global Sustainability Research Global Scientific Research 15 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. Global Sustainability Research Global Scientific Research 16 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 Global Sustainability Research Global Scientific Research 17 Table 9. D-H Casuality Test Null Hypothesis Observation Prob. LGDP β‰  LLCF 91 0.0039 LLCF β‰  LGDP 0.1135 LGDP2 β‰  LLCF 91 0.0062 LLCF β‰  LGDP2 0.1163 LAI β‰  LLCF 91 0.0208 LLCF β‰  LAI 0.0409 LFA β‰  LLCF 91 0.0527 LLCF β‰  LFA 0.5953 LGOB β‰  LLCF 91 0.0148 LLCF β‰  LGOB 0.9493 LURBA β‰  LLCF 91 0.0609 LLCF β‰  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 Global Sustainability Research Global Scientific Research 18 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. Global Sustainability Research Global Scientific Research 19 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. References Abdulla Al Shiam, S., Mohammad Ridwan, Mahdi Hasan, M., Akhter, A., Shamsul Arefeen, S. M., Hossain, M. S., Abir, S. I., & Shoha, S. (2024c). 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