







































Global Sustainability Research                                                     ISSN: 2833-986X 
https://doi.org/10.56556/gssr.v3i3.971 

                                                                  

 
 

Global Scientific Research  27 
 

RESEARCH ARTICLE  

Leveraging AI for Promoting Sustainable Environments in G-7: The Impact of 

Financial Development and Digital Economy via MMQR Approach 

Mohammad Ridwan1*, Shewly Bala2, Sarder Abdulla Al Shiam3, Afsana Akhter1,  

Md Mahdi Hasan3, Md Asrafuzzaman3, Shake Ibna Abir4, Shaharina Shoha4, Robeena 

Bibi5 

 
1Department of Economics, Noakhali Science and Technology University, Sonapur, Noakhali-3814, Bangladesh  
2Department of Finance, University of Dhaka, Bangladesh 
3Department of Management -Business Analytics, St Francis College, USA 
4Department of Mathematics, Western Kentucky University, Bowling Green, KY, USA 
5School of Public Administration, Hohai University, Nanjing China 
 

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

Received: 11 August, 2024, Accepted: 01 September, 2024, Published: 01 September, 2024 

 

Abstract 

This study investigates the role of Artificial Intelligence (AI) in promoting a sustainable environment within the 

G-7 countries by testing the Load Capacity Curve (LCC) hypothesis. Additionally, it examines the effects of 

financial development, the digital economy, and urbanization on the load capacity factor using data from 2010 to 

2022. The research employs cross-sectional dependence and slope homogeneity tests, revealing issues of cross-

sectional dependence and heterogeneity. Panel unit root tests, both first and second generation, confirm that the 

variables are free from unit root problems. Furthermore, panel cointegration tests indicate that the variables are 

cointegrated in the long run. To assess the impact of the explanatory variables on the load capacity factor, the study 

utilizes the Method of Moments Quantile Regression (MMQR). The findings reveal a U-shaped relationship 

between income and the load capacity factor, supporting the LCC hypothesis in the G-7 region. The results also 

indicate that AI innovation and financial development have a significant positive correlation with the load capacity 

factor. In contrast, the digital economy and urbanization are found to significantly reduce the load capacity factor. 

Robustness checks, including the Driscoll-Kraay standard error, Augmented Mean Group, and Common 

Correlated Effect Mean Group estimation approaches, validate the findings obtained from the MMQR method. 

Moreover, the Dumitrescu-Hurlin (D-H) causality assessment is utilized to explore the causal connections between 

variables. The results reveal a unidirectional causal relationship between income and the load capacity factor. 

Additionally, bidirectional causal relationships are the remaining explanatory variables and load capacity factors. 

 

Keywords: Artificial Intelligence; Digital Economy; Financial development; LCC Hypothesis; MMQR approach 

Introduction 

The urgency for improving the standard of ecological sustainability across national, regional, and global economies 

has attracted attention in recent years from policymakers and researchers (Ibrahim & Ajide, 2021, Raihan et 

al.2024a). It has been recognized that one vital cause of climate change is greenhouse gas emissions (Atasoy, 2017; 



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Destek and Sarkodie 2019, Islam et al.2023). About 75% of global emission statistics are attributed to carbon 

dioxide (CO2) emissions, making it among the primary contributors to GHG emissions (Diffenbaugh, 2020, Pattak 

et al.2023). A reduction in emissions of less than 25% and 55%, respectively, must be achieved to meet the goal 

of reducing climate change to less than 2 °C and 1.5 °C by 2030 (UNEP, 2019, Raihan et al.2022a). To prevent 

devastating global climatic catastrophes, COP26 suggested that all nations aggressively embrace equitable growth 

methods and keep global temperature change to 1.5 °C (Murshed, 2021, Raihan et al.2023a). The seven nations 

considered vital for preserving a high standard of living in the world's economy are Japan, the US, Canada, Italy, 

Germany, France, and the UK (Saeed et al., 2024, Voumik and Ridwan, 2023). Several factors led to the selection 

of G-7. For example, The G-7 territory produces more than 60% of the globe’s net financial assets as a consequence 

of their considerable economic endeavors (Alola et al., 2022). Then, the group's total usage of energy accounts for 

over 42% of global energy use (World Bank 2017). Then, through its annual conference, the group of seven has 

devoted itself for the past 20 years to developing green technology and reducing the generation of waste (Kirton, 

2012, Raihan et al.2023b). Fourth, environmental damage remains an imminent threat to the G-7 countries, even 

with their advancement toward a sustainable economy (Khan et al., 2020). For example, the group produced around 

38% of the total global emissions between 1960 and 2014 (World Bank 2017). Canada holds the greatest per capita 

energy use and GHG pollution among the G7. The country's performance regarding environmental policy is 

assessed as average because it continues to offer incentives for the use and extraction of fossil fuels. In terms of 

power consumption and release of greenhouse gases, the UK, Italy, and Germany do exceptionally well, while the 

USA and Japan perform quite poorly (Hao et al., 2020, Raihan et al.2022b). The non-homogeneous features of the 

nation make the representation seem fascinating. The findings of this research could assist the leading nations 

develop appropriate environmental strategies.  

The G-7 nations, which have created complex nations, are growing remarkably in response to technological 

advances and changes in society (Balsalobre-Lorente et al.,2024). These economically advanced countries possess 

more complicated economies than other countries (Khan et al., 2022, Ridwan, 2023). The load capacity factor 

(LCF) is a substitute for the ecological condition in this work, which relies on conclusions from previous 

examinations by Wang et al. (2024), Voumik et al. (2024), Awosusi et al. (2022), and Shang et al. (2022). A 

detailed look at the needs imposed on air, land, and water by human beings, as well as the ability of earth's resources 

to meet and adapt to such demands, is made feasible by the LCF. One technique for assessing how sustainable 

human activity is concerning the earth's carrying capacity is the ecological footprint (EFP) accounting 

(Wackernagel & Rees, 1998; Hoekstra, 2009). Ecological footprint (EFP) and biocapacity are both of its 

components (Mir et al., 2022). Environmental deterioration puts the well-being of almost 80% of the world's 

population at risk (Wu et al., 2024). The past ten years have witnessed notable GDP expansion and achievement, 

especially in developing nations. This has placed a growing strain on ecological systems, resulting in biodiversity 

loss and a spike in greenhouse gas emissions (Esmaeili et al., 2023; Ayad et al., 2023; Yameogo et al., 2021). 

Numerous opportunities through which the financial sector might impact environmental quality have been 

discovered by previous research. For instance, liberalization of finance increases the volume of money that 

consumers and companies must allocate and consume, which results in a spike in pollution (Bekhet et al., 2016; 

Awosusi et al., 2021); and financial accessibility promotes excessive energy usage and investments in green 

technologies, which enhances green environment (Tamazian & Rao, 2010, Ridzuan et al.2023). With the 

development of IoT, AI, virtual reality (VR), blockchain, autonomous vehicles, and modern technologies, the 

digital economy is expected to become indispensable (Javaid et al.,2022; Tolstykh et al., 2019). Strong online 

payment verification is one example of how digital technologies have simplified transactions (Castelo-Branco et 

al., 2019). Out of the three categories, ecological sustainability has the highest opportunity to reap benefits from 

AI, with 93% of the SDG targets being positively impacted (Vinuesa et al., 2020, Urbee et al.2024). AI might 



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positively affect 79% of the SDGs. The Intergovernmental Panel on Climate Change (IPCC) highlighted that global 

pollution must drop to net zero by at least 2050 to preserve a "high confidence" level of limiting warming to 

manageable degrees (Masson-Delmotte et al., 2018). By 2030, AI-powered technologies are projected to help 

reduce worldwide emissions by 4% (Gawel & Herweijer, 2021, Voumik et al.2023a). According to certain 

research, urban environmental sustainability advances the digital economy (Ulucak & Khan 2020; Shobande & 

Ogbeifun 2021; Mondejar et al. 2021). However, ecological sustainability might decline as a result of the digital 

economy (Cheng et al. 2019; Avom et al. 2020). By connecting all aspects of business across the Internet, Moriset 

and Malecki (2009) contend that the DGE lessens reliance on physical locations. 

With this background in mind, our investigation aims to figure out how the G-7 countries (Canada, France, 

Germany, Italy, Japan, the United Kingdom, and the United States) are affected by financial development (FD), 

artificial intelligence (AI) innovation, GDP growth, and the digital economy (DGE) sing MMQR Approach from 

1990 to 2018. Moreover, the DKSE, AMG, and CCEMG methodologies were employed to verify the reliability of 

the results. The motive is to deliver advanced ideas of the multifaceted relationships between these 

components while supporting the emergence of ecologically friendly methods and stable economic development 

in this region. In financial development, this investigation fills the need for more information about the LCF in the 

G-7 nations in light of AI innovation and the digital economy. Previous research highlighted the impacts of 

digitalization, emerging technologies, and globalization in finance on LCF. Other nations or areas have conducted 

multiple studies on LCF and associated variables. This research adds uniqueness to the G-7 economies instance. 

As far as we are aware, the experiment we undertook is the initial effort to conduct a thorough analysis of the 

literature on the LCF, and its significance comes from the fact that AI and DGE have not received much attention 

in previous research projects. Policymakers and strategy developers may be able to foster environmentally 

conscious behavior more effectively if they are aware of these elements.  

The other part of this study falls into five sections. Section 2 covers the body of current literature. In part 3, we go 

over the data, method, and modeling. Section 4 provides research outcomes and discussions. Finally, section 5 

reports its conclusion and suggestions for policymaking. 

  

Literature Review 

In various regions of the world, the complex interrelationships among ecological footprint, clean energy use, 

urbanization, and financial globalization have been the subject of several ongoing researches. We want to 

emphasize the innovative sides of our research, which we believe add value to this area of inquiry that is 

consistently expanding. As a result, the findings of previous studies that have shed light on the factors influencing 

economic growth, financial development, artificial intelligence (AI), urbanization, the digital economy, and 

environmental sustainability in the Group-7 countries have been compiled into six sub-sections within this 

component. 

Since early economies prioritized increased production as a basis for the betterment of people, economic growth 

and a sustainable ecosystem are strongly connected (Kihombo et al.,2022, Voumik et al.2023b). Global researchers 

have been paying careful consideration to the link that exists between economic development, structural change, 

and environmental pollution in recent years (Dong et al., 2020). Environmental deterioration was revealed to be a 

result of higher GDP by Xue et al. (2022) utilizing the ARDL approach. In a similar vein, Bekhet al. (2017) 

observed that in Saudi Arabia, Oman, Qatar, and Bahrain, GDP development is linked to greater CO2 emissions. 

Additionally, the findings that a 1% increase in the Italian GDP over a longer period might turn into an 8.08% 

increase in CO2 pollution (Pattak et al.,2023). Multiple investigations also agreed with this outcome, such as Ali 

et al.(2020) in Malaysia, Voumik et al.(2023c) in Kenya, Ahmed et al.(2015) in Pakistan, Ahmed et al.(2020) in 

China and Saud et al.(2018) in BRI countries. Nevertheless, based on research conducted in the G-7, Balcilar et 



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al. (2018) assert that environmental quality in Germany and the UK is unaffected by growth in the economy. 

However, Ridwan et al. (2024) claim that both in the short and long run, GDP significantly lowers CO2 emissions 

in six South Asian economies. Raihan et al. (2024b) found that economic expansion somewhat reduced carbon 

dioxide emissions in their examination of the link between GDP growth and CO2 emissions in India. Similarly, 

Bento and Moutinho (2016) examined data for Italy from 1960 to 2011 using the ARDL technique and determined 

that GDP development lowers pollution levels in Italy. Moreover, Mehmood et al. (2023) examined the GDP 

stimulus of the Group of Seven area's efforts to reduce greenhouse gases from 1990 to 2020. This CS-ARDL study 

shows an inverse association between GDP and CO2 emissions.  

The term artificial intelligence refers to automated cognition that was developed in the 1950s and has discovered 

effective applications in research as well as business (Hoang et al., 2022). Artificial intelligence (AI) can encourage 

sustainability across multiple sectors by reducing waste products, increasing asset availability, and delivering green 

solutions (Rakha, 2023). Blockchain technology and artificial intelligence (AI), in particular, can significantly 

sever the link between economic expansion and its detrimental effects on the natural world (Jiang et al., 2021; 

Meng and Zhao, 2022; Tsolakis et al., 1523). There are several advantages of using AI-powered sensors and 

equipment for real-time hazardous substance monitoring in-ground and plant matter (Singh & Kaur, 2022). 

Initially, in contrast to conventional experimental techniques, it enables the more accurate and consistent 

recognition of these chemicals. Second, it provides information in real time, allowing fast reactions to any potential 

contamination problems. Finally, it decreases the requirement for manual collection of information and analysis, 

which lessens manpower and improves the monitoring of the accuracy of processes (Jeong and Choi, 2022). Al-

Sharafi et al. (2023) gathered information from Malaysia and Turkey to explore the factors influencing the adoption 

of AI products and their effect on environmental conditions. They found that while AI solutions can reduce 

expenses, conserve water and energy, and enhance the disposal of waste, their influence on environmental 

sustainability is rather small, especially in emerging territories. Stakeholders can offer the framework for making 

sensible choices, putting effective methods into practice, and developing strategies that encourage ecological 

sustainability through using AI and AIoT technologies (Bibri et al., 2024). Moreover, governments can promote 

openness, authenticity, and accountability in the manufacturing process by utilizing technology and AI. This will 

help to promote sustainable practices and lessen adverse environmental impacts (Hong and Xiao, 2024). 

Environmental sustainability and financial development are connected in a complicated way, and financial 

progress can lead to environmental damage in certain instances. Nonetheless, robust regulatory bodies and their 

consistent green initiatives might lead to enhanced environmental standards and a healthier economic system 

(Birdsall and Wheeler 1993). Most research shows an adverse link between a sustainable environment and financial 

growth. However, numerous studies also found a positive link based on associated variables, including the nature 

of the industry, country classification, and financial system in the economy (Lyu et al., 2021; Usman et al., 2021; 

Zafar et al., 2019). According to Shahbaz (2013), in Pakistan, environmental degradation might worsen as a result 

of financial uncertainty. By organizing countries into three income categories, Nasreen and Anwar 

(2015) discovered that, within the low-income panel, financial development (FD) increased damage to the 

environment, while in the high-income panel, it declined. According to Sharma et al. (2020), rising Asian nations' 

environmental impacts are positively influenced by financial growth. In addition, by raising the EF, financial 

development causes environmental damage in emerging regions (Ahmad et al., 2022). As per certain analyses 

(Zhang, 2011; Shahbaz et al., 2016), expansion in finances protects the level of the ecosystem by reducing 

emissions of greenhouse gases. Using OLS, PQR, and CCEMG methodologies, Ali et al. (2023) reviewed the 

effects of financial development on the ecological health of the E-7 bloc. They concluded that the region's 

environmental deterioration is exacerbated by financial development. Financial growth, based on Lv and Li (2021), 

can enhance the level of biodiversity, specifically in areas where it becomes more expanded and developed. 



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Furthermore, Shoaib et al. (2020) observed that financial expansion had an uplifting impact on CO2 emissions in 

G8 and D8 territories employing the pooled mean group technique. However, Dogan and Turkekul (2016) 

illustrated that monetary improvement is not a factor in the US's ecological degradation. 

The term "digital economy" refers to economic activity, including online transactions across various platforms and 

technologies, including mobile, large-scale data, the Internet, and information and communications technology 

(Javaid et al.,2024). Many investigations on the financial and social implications of DGE have been conducted by 

academics. From a small-scale perspective, DGE might reduce both the degree of information asymmetry and 

alleviate businesses' financial limitations by utilizing modern digital technologies. Liu (2023) suggests that the 

expansion of the DGE may have a moderating implication on discharges of pollution. Raihan et al. (2024c) 

conducted research in the G-7 region from 1990 to 2019 to check the consequences of DGE on carbon emission. 

Utilizing the ARDL model showed that the digital economy significantly promotes a green ecosystem. Jiang et al. 

(2024) illustrate that the digital economy ensures environmental sustainability and can cut emissions by up to 0.092 

%. Yuan et al. (204) adopted the spatial econometric model based on panel data from 267 Chinese cities from 2012 

to 2021. They found that DGE could significantly lower harmful emissions. Similarly, several studies in different 

areas also found that (Ma et al.,2024; Dong et al.,2022; Bai et al.,2022; Che and Wang, 2022; Li et al.,2022). The 

influence of a shared digital economy on environmental pollution is yet unknown (Jin et al.,2018). According to 

Kuntsman and Rattle (2019), the improvement, maintenance, and disposal of modern innovative technologies have 

all had a substantial detrimental impact on natural health. According to Zha et al. (2022), the growth of the digital 

economy can successfully enhance the environmental sustainability of neighboring cities while simultaneously 

mitigating the CO2 pollution intensity of a particular region. In developing nations, DGE increased carbon 

emissions, based on Danish et al.(2019), who used information from 73 countries and the modified ordinary least 

squares (OLS) approach. 

The phenomena of urbanization have a noteworthy implication on the condition of the natural world. The greater 

need for energy, resources from nature, and additional services generated by urbanization will eventually have a 

detrimental impact on the ecosystem. Numerous authors, such as Arshad et al. (2020) for Asian economies, 

Nathaniel et al. (2021) for Latin American and Caribbean countries, Van et al. (2018) for Australia, and Mahmood 

et al. (2020) for Saudi Arabia, have explored the link among urbanization, GHG emissions, and the environmental 

impact. Arslan et al.(2022) indicated that urban population expansion accelerates ecological deterioration. For 

ASEAN nations, Nathaniel and Abdul (2020) investigated the link between urbanization and ecological footprint 

(EFP) between 1990 and 2016. According to their research, urbanization increases the EFP, which reduces 

environmental sustainability. Utilizing unique panel data approaches from 2000 to 2020, Feng and Li (2024) 

demonstrated that urbanization continues to be a leading element in rising ecological degradation in the ASEAN-

6 economies. Surprisingly, Xue et al. (2022), utilizing the ARDL approach, discovered that urbanization reduced 

pollution in the third-largest European country, France, from 1987 to 2019. Ahmad et al. (2021) demonstrated that 

urbanization improves ecological sustainability by lowering the ecological footprint, especially measured by 

FMOLS and DOLS. Mehmood (2021) discovered that urbanization in the SAARC area increased air quality 

between 1996 and 2015. Moreover, Azam and Khan (2016) investigated how urbanization affected environmental 

degradation in Bangladesh, India, Sri Lanka, and Pakistan between 1982 and 2013. The outcomes showed that, 

while there was a negative correlation in Bangladesh and India, there was an encouraging correlation in Sri Lanka 

and Pakistan between urbanization and environmental degradation.   

The methods of knowledge accumulation related to the digital economy (DGE), artificial intelligence (AI) 

innovation, and the real effect of financial growth on load capacity factor (LCF) remain unclear, regardless of the 

G-7 nations' promising sustainable environment quality. From the standpoint of the G-7, aspects like artificial 

intelligence (AI), the digital economy, and financial development are entirely novel fields of investigation. 



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Furthermore, the MMQR Approach, which has not been widely implemented in prior LLC studies, is used in our 

work. This approach enables the effective assessment of data from panel models, consequently augmenting the 

discipline's methodological understanding. Through an analysis of all of these standards, the nations selected can 

assess whether harnessing innovations in technology, economic collaboration, and sustainable growth may offer 

the possibility to improve their sustainability issues. Therefore, by examining the dynamic impacts of GDP, DGE, 

FD, and AI on LCF and employing different advanced econometric methodologies, this present study intends to 

close the deficiency in the literature for the instance of Group Seven nations. 

 

Methodology 

Data and Variables 

The research gathers yearly data for the G-7 from four sources for the years 1977–2018. Our World in Data 

indicates that the digital economy indicates ICT goods imports (% of total goods imports), and artificial intelligence 

reflects patent applications in the field of AI. The World Bank provided the statistics used to calculate GDP and 

GDP2, which are expressed as (Current US$). The load capacity factor (LCF) (biocapacity/ecological footprint) is 

derived from data from the Global Footprint Network. Lastly, the IMF's Financial Development Index is a 

collection of financial development data. 

 

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 

FD Financial 

Development 

LFD Financial 

Development Index 

IMF 

DGE Digital Economy LDGE ICT goods imports 

(% of total goods 

imports) 

Our World in Data 

URBA Urbanization LURBA Urban Population 

(% of total 

population) 

WDI 

 

Theoretical Framework 

The LCC theory is based on the LCF indicator, which takes alternatives for environmental provision and manmade 

needs for the environment into account. The LCF initially appeared in the literature by Siche et al. (2010), and 

Pata (2021) was the very first to do empirical studies on the variables that influence the LCF. By differentiating 

the ecological footprint and biocapacity, the LCF enables thorough environmental quality assessment (Dogan and 

Pata,2022). The LCF enables both the supply and demand sides to examine the ecosystem, and a more favorable 



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environment is shown by a higher LCF (Pata and Kartal, 2023). Scholars have developed advanced techniques to 

measure the implications of manmade activities on ecology and evaluate how long the planet will survive these 

effects. With the use of these methods, the LCC evolves into an invaluable instrument for integrating intricate data 

into an accessible framework by considering the association between human actions and the clean environment. 

The components of GDP growth, financial development, artificial intelligence (AI), urbanization, digital economy, 

and load capacity factor may be related in several ways, as already mentioned. For the LCC hypothesis, we have 

developed the following equation (1) to expand our understanding of prior studies: 

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

Here, Kt is a factor for additional parameters impacting the LCF, while GDP is a variable for income in equation 

(1). Equation (2) tries to illustrate a broader understanding of the elements impacting the LCF. 

  

LCF = f(GDP, GDP2, AI, FD, DGE, URBA)                                 (2)  

While the labels financial development (FD), artificial intelligence (AI), urbanization (URBA), and digital 

economy (DGE) serve to depict particular concepts, the load capacity factor in equation (2) is portrayed by LCF. 

An econometric explanation of this equation is provided below. 

 

LCFit = ρ0 + ρ1GDPit + ρ2GDPit
2 + ρ3AIit + ρ4FDit + ρ5DGEit + ρ6URBAit   (3) 

The logarithmic outcomes of the elements are displayed in equation (4), which improves interpretation and 

simplifies the formulation of statistical findings. These transformations can deal with heteroscedasticity and accept 

information with various magnitudes, making them effective in reducing the implications of data with an extensive 

range. Moreover, the research's coefficients are represents in the parameter range of ρ0 to ρ6 in equation (4). 

 

LCFit = ρ0 + ρ1LGDPit + ρ2LGDPit
2 + ρ3LAIit + ρ4LFDit + ρ5LDGEit + ρ6LURBAit   (4) 

Econometric Framework 

The link between LCF and some independent factors in the G-7 nations was investigated in this study using the 

MMQR approach for data estimation. We additionally employed the DKSE, AMG, and CCEMG approaches to 

guarantee robustness. To begin with, we checked for dependence and stationarity using the CSD and Panel unit 

root analyses. We subsequently carried out the cointegration and MMQR estimation. Finally, the D-H causality 

method was utilized to determine the causal links among the selected factors.  

 

Cross-Sectional Dependence test 

Cross-sectional dependence (CSD) can arise from unidentified reasons that distort the correct values, thereby 

decreasing the usefulness of panel data. When the CSD problem is avoided in panel data, can end up in misleading 

outcomes (Waris et al., 2023). As countries grow increasingly integrated and dependent on one another, 

industrialization is making CSD a greater problem in panel data (De Hoyos and Sarafidis, 2006). Our study will 

initially utilize the CSD testing created by Pesaran (2015) in each cross-sectional unit to predict the existence of 

CSD before starting the empirical portion of the investigation. So, in order to illustrate  the test’s statistics, the 

below equation can be taken: 



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𝐶𝑆𝐷 = √
2𝑇

𝑁(𝑁−1)𝑁
(∑ ∑ 𝐶𝑜𝑟𝑟𝑖,𝑡

̂𝑁
𝐾=𝑖+1

𝑁−1
𝑖=1 )……………………… (5) 

Panel Unit root test 

We used the LLC test, developed by Levin et al. (2002), and the second-generation panel unit roots 

assessment, introduced by Pesaran (2007), taking into account any potential cross-sectional independence within 

the panel time-series information. These tests include the cross-sectional augmented Dickey-Fuller (CADF) panel 

unit root tests and the cross-sectional IPS (CIPS), which is an extension of the IPS panel unit root test that was 

established by Im et al. (2003). Based on the alternative hypothesis that at least one individual series in the panel 

is stationary and the null hypothesis that all individual series within the panel are stationary, the CIPS and CADF 

panel unit root methods are conducted (Ssali et al.,2019). The LLC test statistics can be displayed in the following 

way: 

 

∆yit = 𝛽𝑖yit−1 + ∑ dij∆yit−1 + Mit
′ φϑi

j=1 + μit    ………………………….(6)                                  

                                      

Here,  𝑀𝑖𝑡
′   represents the column vector of the independent variable and in regression 𝜑 denotes the vector of 

parameters. 

By incorporating heterogeneity in the coefficient of yi,t−1, Im et al. (2003) expanded the LLC test and introduced 

a test strategy named the IPS unit root test that utilized the mean of each participant's unit root statistics. The 

equation underlying the IPS unit root test is as follows: 

ΔYi,t = βi + γi,t + δyi,t−1 + ∑ θk∆yi,t−j + μi,t
k
j=1                                        (7) 

In contrast to traditional unit root tests, Pesaran's (2007) novel approach, CIPS, enables heterogeneity resilient to 

CSD and provides more consistent and trustworthy findings (Harris and Tzavalis 1999; Im et al. 2003; Levin et al. 

2002). The CIPS test is examined using equation (8): 

 

𝐶𝐼𝑃𝑆 =  
1

𝑁
∑ 𝑡1(𝑁, 𝑇)𝑁

𝑡=1 ……………………………… (8) 

Where N means a cross-sectional aspect, and T means time series dimension. Moreover, equation (8) provides the 

following method for computing the CADF: 

∆𝑌𝑖𝑡 =  𝛿𝑖 +  𝜌𝑖𝑌𝑖𝑡−1 + 𝛿𝑖�̅�𝑡−1 + ∑ 𝜔𝑖𝑗�̅�𝑡−1

𝜗

𝑗=1
+ ∑ 𝛼𝑖𝑗

𝑝

𝑗=1
∆𝑌𝑖,𝑡−1 + 휀𝑖𝑡 … … … (9) 

Where �̅�𝑡−1 and ∆𝑌𝑖,𝑡−1 symbolize the average values of the cross-sectional analysis for both the first difference 

and lag. 

Panel Cointegration test 

The Pedroni panel cointegration examination is utilized to check if cointegration prevails, assuming panel 

heterogeneity (Raihan et al.,2024). Pedroni's (1999) panel cointegration test is utilized in this work, which has two 

separate assessments. Panel v-statistics, panel rho-statistics, panel PP-statistics, and panel ADF-statistics are the 



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four statistical measures used in the first test, which uses a within-dimension method. Group rho-statistics, group 

PP-statistics, and group ADF-statistics are the three statistical measures used in the second test, which employs a 

between-dimension technique. Both the homogeneous (panel) and interdimensional (group) versions of Pedroni's 

statistics are of the ADF and PP categories (Lugo-Arias et al., 2024). The null hypothesis that there exists no 

cointegration is rejected if the majority of these data have p-values that are less than a predetermined significance 

threshold. The general regression residuals for the proposed co-integration regression are provided below: 

𝑦𝑖,𝑡 =  𝜔𝑖 + 𝛿𝑖𝑡 + 𝜌1𝑖𝑥1𝑖,𝑡 + 𝜌2𝑖𝑥2𝑖,𝑡 + ⋯ + 𝜌𝑀𝑖𝑥𝑀𝑖,𝑡 + 휀𝑖,𝑡………………………………. (10) 

for, t = 1,…..T;  i = 1,…, N; m = 1,…, M 

The null hypothesis of the no-cointegration test is denoted as 𝐻0 : 𝑓𝑖 = 0; ∀𝑖 (absence of cointegration).  

Method of Moments Quantile Regression 

This study uses Machado and Silva's (2019) invention, the moments' regression approach (MM-QR), to determine 

the quantiles. In contrast to conventional quantile regressions, the MMQR evaluates the objectives by modifying 

the mean values, allowing "conditional heterogeneous covariance implications" by Koenker (2004) and Canay 

(2011) to affect and accomplish the dependent variable's obtaining over the whole data distribution. Furthermore, 

this technique allows for location-dependent imbalance as the variables are sensitive to their position within the 

distribution circumstances (Hieu and Mai,2023). All these suggest that the MMQR is more acceptable and robust, 

especially when it comes to addressing the issues of heterogeneity and endogeneity (An et al., 2021) and 

developing asymmetrical nonlinear associations (Elbatanony et al., 2021). The alternative model is listed as 

follows: 

𝑌𝑖𝑡 = 𝛽𝑖 + 𝑋𝑖𝑡𝛾 + (𝜗𝑖 + 𝑍𝑖𝑡𝜑)𝑈𝑖𝑡………………………..(11) 

Where the probability is 𝑃 {𝜗𝑖 + 𝑍𝑖𝑡𝜑 > 0} = 1. Additionally, parameters such as 𝛽, 𝛾, 𝜗, and 𝜑 are required to 

be calculated. Moreover, in 𝛽𝑖, 𝜗𝑖𝑖 = 1…….., n denotes ‘individual fixed effect, and Z represents the k vector of 

component X”. Furthermore, the elements are transformed with component m given below: 

𝑍𝑚 = 𝑍𝑚 (𝑋), 𝑚 = 1, … … … , 𝑘………………………(12) 

Here, 𝑈𝑖𝑡 is orthogonal to 𝑋𝑖𝑡 and consistent in fulfilling the moment conditions, which do not include stringent 

heterogeneity. Hence, the conditional quantile of Y is mentioned below: 

𝑄𝜏 (
𝜏

𝑋𝑖𝑡
) = (𝛽𝑖 + 𝜗𝑖𝑞 (𝜏)) + 𝑋𝑖𝑡𝛾 + 𝑍𝑖𝑡𝜑𝑞(𝜏)  ………………….(13) 

In this equation, 𝑋𝑖𝑡 symbolizes the predictive variables such as GDP, 𝐺𝐷𝑃2, AI, FD, DGE, and URBA. On the 

other hand, 𝑌𝑖𝑡 represents the dependent variable like LCF. It's important to keep in mind that changing the intercept 

does not take into account a person's influence when OLS is utilized, and a consequence comes from fixed effects. 

Now, 𝑄(𝜏) can be estimated as follows: 

𝑀𝑖𝑛𝑞 = ∑𝑡∑𝑖 𝑝𝜏(𝑅𝑖𝑡 − (𝜗𝑖 + 𝑍𝑖𝑡𝜑𝑞))…………….(14) 

Robustness Check 

This phase involves performing three separate tests: the DKSE introduced by Driscoll and Kraay (1998), the AMG 

estimator created by Bond and Eberhardt (2009) and Eberhardt and Teal (2011), and the CCEMG estimator 

introduced by Pesaran (2006). In contrast to traditional standard errors, DKSE takes into consideration slope 



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heterogeneity, average CSD, and correlated errors that might occur between observations. This helps to reduce the 

possibility of biases and inefficiencies during parameter estimation (Ridwan et al., 2024). Because of the slope of 

heterogeneity and the CSD problem, we thus utilized the AMG estimator in line with Nathaniel et al. (2020); 

and Murshed et al. (2021). Furthermore, because AMG considers the endogeneity issues and calculates the 

elasticity of particular nations, it offers several advantages over conventional panel methodologies (Isik et al., 

2021). In addition, findings are better suited when the time exceeds the cross-sectional unit (Shahzad and Aruga, 

2023). The identification challenge is resolved by the CCEMG by taking into consideration temporal variations 

with various pitch variables (Raihan et al.,2024). The AMG is a specific way of treating CCEMG that takes into 

account yearly inefficiency and neglected aspects in addition to cross-dependence, heterogeneity, and structural 

and technology developments (Polcyn et al., 2023). 

D-H Causality Test 

Causality tests are necessary to determine the consequences of particular policies that address environmental 

pollution. Granger (1969) established an assessment for assessing the elements' causal connections. In our present 

work, we utilize the D-H panel causality examination, an advanced measure of causality that was created by 

Dumitrescu and Hurlin (2012). This method is extremely comprehensive and beneficial in producing reliable 

outcomes throughout CSD as it allows for both N > T and T > N samples (Ahmed and Le, 2021). According to 

Dumitrescu and Hurlin (2012), the alternative hypothesis asserts that there is at least one Granger causality 

relationship between cross-sections, while the null hypothesis claims that there is no Granger causality link 

between cross-sections. The causality of the D-H panel may be written as: 

yit = θi + ∑ δi
j
yi(t−j) + ∑ γi

j
xi(t−j) +

j
j−1

j
j−1 εit                 (15) 

Here, x and y represent the observables, δi
j
  represents the autoregressive parameter, and γi

j
 denotes the estimations 

of the regression coefficients 

Results and Discussion 

Summary Statistics 

Based on 91 observations, the summary statistics for the factors we explored can be seen in Table 02 below. A 

total of seven factors are presented in the G-7 nations' descriptive statistics: LLCF, LGDP, LGDPSQ, LAI, LFD, 

LDGE, and LURBA. All of the chosen variables—aside from LLCF and LFD—have positive means, as the table 

illustrates. Furthermore, each variable's estimated standard deviation is small, suggesting that the data points are 

mostly concentrated around the mean with minimal fluctuation. While LFD and LURBA suggest negative 

skewness, a lot of variables show positive skewness. The Jarque-Bera normality assessment was applied to 

establish that all the variables had a normal distribution that took skewness and kurtosis into consideration. 

  

Cross-Sectional Dependence test 

To confirm if the CSD is present in our data set or not, Table 03 displays the results of the CSD assessment. The 

CSD statistics for all variables are regarded as statistically significant at conventional levels because of the 

extremely low p-values. All variables have p-values of 0.000, except LFD, where the p-value is 0.043. Taken 

together, these data provide strong evidence of cross-sectional correlation at the 1% level. It indicates that our data 

collection contains a CSD problem. Thus, we may infer that there is a cross-sectional dependence problem with 

lnLCF, lnGDP, lnGDP2, lnAI, lnFD, lnDGE, and lnURBA. 



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Table 2: Summary statistics of the variables 

Statistic LLCF LGDP LGDP2 LAI LFD LDGE LURBA 

Mean -0.190398 10.69067 114.3221 5.522956 -0.190398 2.120523 4.387441  

Median -1.121269 10.67368 113.9275 5.513429 -0.178789 2.056207 4.397432 

Maximum 0.723357 11.24282 126.4009 9.709417 -0.06901 2.670226 4.521299 

Minimum -2.038284 10.317 106.4405 1.609438 -0.400781 1.569931 4.224305 

Std. Dev. 0.779588 0.178842 3.840701 2.003691 0.08979 0.316546 0.075888 

Skewness 0.811059 0.519595 0.575074 0.220978 -0.496358 0.255469 -260761 

Kurtosis 2.868482 3.496751 3.583357 2.426018 2.141739 2.09277 3.070021 

Jarque-Bera 10.04246 5.030319 6.306099 1.989793 6.529623 4.110635 1.049867 

Probability 0.006596 0.08085 0.042722 0.369762 0.038204 0.128052 0.591595 

Sum -85.88593 972.851 10403.31 502.589 -17.32621 192.9676 399.2571 

Sum Sq. Dev. 54.69818 2.878612 1327.589 361.3301 0.72561 9.018143 0.51831 

Observations 91 91 91 91 91 91 91 

 

Table 3: Results of Cross-Sectional Dependence test  

Variables CD Statistics P-Value 

LLCF 4.84*** 0.000 

LGDP 5.36*** 0.000 

LGDP2 5.37*** 0.000 

LAI 13.33*** 0.000 

LFD 3.06** 0.043 

LDGE 8.73*** 0.000 

LURBA 16.21*** 0.000 

 

Panel Unit root test 

Table 4 presents the findings from the unit root testing. The LLC and IPS assessments were the first-generation 

unit root methods, and the CIPS and CADF analyses were the second-generation unit root tests employed in this 

investigation. LAI, LFD, and LDGE are the only ones that show stationary behavior at the level form I(0), based 

on the LLC test findings. At the 1% significance threshold, all of the remaining elements are significant and 

stationary at the first difference form I(1). The results of the IPS test suggest all other variables are stationary at 

the first difference form, except LFD and LDGE. On the other hand, LFD and LDGE are significant at the 1% and 

5% levels of significance, respectively, and stationary at the level form. The other factors (LLCF, LGDP, LGDP2, 

LFD, and LURBA) are stationary at the first difference form I(1) and significant at the 1% level of significance, 

according to the CIPS test, while LAI and LDGE are stationary at the initial level and significant at the 5% level 

of significance. 

Comparably, the CADF unit root test reveals that all variables, except for LAI and LFD, which are stationary at 

the level form I(0) and significant at the 5%, are stationary at the first difference form I(1). Therefore, before we 

considered their initial differences, LCF, GDP, GDP2, and URBA were not stationary; as a result, they became 



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stationary in all four unit root evaluations. Conversely, in the level form I(0), LAI, LFD, and LDGE are stationary. 

We can move on with the assessment utilizing the MMQR framework because of this mixed order of integration. 

 

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 -2.392 -5.032*** -0.801 -3.457*** -0.629 -3.968*** -1.032 -3.781*** I(1) 

LGDP -0.742 -5.749*** -1.216 -4.407*** -1.532 -3.409*** -1.231 -3.801*** I(1) 

LGDP2 -0.767 -5.759*** -1.415 -4.016*** -1.288 -3.894** -1.517 -3.776*** I(1) 

LAI -5.800*** -5.103*** -1.251 -4.001*** -2.243** -4.098*** -3.023** -3.838*** I(0) 

LFD -5.061*** -6.091*** -3.245** -4.600*** -1.078 -3.558*** -2.981** -3.812*** I(0) 

LDGE -5.009*** -6.881*** -3.980*** -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) 

 

Panel Cointegration test 

The findings of the Panel cointegration examination are presented in Table 05. Two distinct sets of alternative 

hypotheses are evaluated in the test: one for common autoregressive coefficients within and one for individual 

autoregressive coefficients between dimensions. 

The Panel rho-statistic is positive but not statistically significant; in addition, the Panel v-statistic is positive but 

not statistically significant, indicating mixed proof for cointegration. Nonetheless, the extremely significant Panel 

PP and Panel ADF-Statistic results provide strong evidence to reject the null hypothesis that there is no 

cointegration. The null hypothesis that there is no cointegration across panels is strongly rejected by the Group PP 

and Group ADF-Statistic, which are both considerably negative. Under the assumption of individual autoregressive 

coefficients between dimensions, the Group rho-statistic is positive but not significant. The analysis shows that 

there is still an indication of cointegration throughout the variables in the overall dataset, even in the context of 

considerable disparities in autoregressive coefficients within and across aspects. 

Table 5: Panel cointegration test 

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

 Statistic Prob. 
Weighted 

Statistics 
Prob. 

Panel v-Statistic 1.86726 0.0309 0.41145 0.3404 

Panel rho-Statistic 1.70292 0.9557 2.97131 0.9985 

Panel PP-Statistic -4.44973 0.0000 -2.07019 0.0000 

Panel ADF-Statistic -5.01670 0.0000 -2.89741 0.0000 

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

  Statistic Prob.   
Group rho-Statistic 4.20741 0.9761   
Group PP-Statistic -5.76269 0.0000   
Group ADF-Statistic -3.27079 0.0000   

 

 



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Method of Moments Quantile Regression 

The outcomes of the MM-QR technique are presented in Table 10. The estimated coefficient of LGDP has a strong 

and negatively significant implication on LLCF, indicating that a growth in per capita GDP greatly upsurges 

environment pollution at all quantile levels. The findings of the study align with the results of Adebayo et al. (2021) 

for South Korea, Orhan et al. (2021) on India, Ridwan et al. (2023) linked to France, Ahmad et al. (2024) regarding 

China, Pata and  Samour (2023) in France, and Adebayo and Rjoub (2021) in terms of the MINT areas. 

Additionally, Destek et al. (2020) discovered that in the G-7, higher GDP is associated with increased emissions 

of CO2 and expressed that greater economic activity which usually entails burning fossil fuels for energy is a direct 

result of economic expansion. Furthermore, Chien et al. (2023) have shown that growth in the economy often 

increases people's financial resources, which raises consumption levels and degrades the environment in the G-7 

region. However, some authors disagree with this consequence and reveal that development in GDP can upgrade 

the environmental quality (Voumik et al., 2022; Nica et al., 2020). The coefficient of LGDP2 is positively 

significant at all quantiles. Our findings of LGDP2 illustrate that financial expansion over time recovers 

environment pollution by adopting clean power and utilizing technology into manufacturing process. The finding 

aligns with the outcome of Bunnag (2023) for Thailand. 

There is a substantial and encouraging relationship between artificial intelligence (LAI) and LLC in all quantiles. 

AI innovation creates better opportunity for individuals to use green technology and highlights the utilization of 

technologies for green ecosystem. By modernizing China's manufacturing facilities, AI can have an advantageous 

systemic impact on lowering the intensity of environmental pollution (Zhao et al.,2023). The use of AI technology 

in ecological damage prevention was also highlighted by Ye et al. (2020). 

Moreover, in all quantiles, the digital economy is inversely and significantly related to LCF. This indicates that a 

digitalized economy is not good for ensuring a sustainable environment in the G-7 area. Wang et al. (2022) show 

that China's CO2 pollution might mitigate as long as the digital economy grows. Similarly, Usman et al. (2021) 

align with this conclusion in certain Asian countries. However, Raihan et al.(2024) opposed this conclusion and 

revealed that in G-7 countries, DGE improves environmental sustainability. Moreover, by influencing residential 

energy decision-making and individual ecologically conscious actions, digital technology can decrease energy use 

and encourage ecologically sound growth (Chiabai et al., 2013; Bastida et al., 2019). 

Conversely, LFD beneficially affects LLCF, which indicates that it promotes a clean environment, and the 

outcomes are significant at 1% across all quantiles. This conclusion conflicts with those of (Xu et al., 2018) in 

Saudi Arabia, Ahmad et al. (2022) in 17 developing nations, Weili et al.(2022) in the Belt and Road countries, and 

Shehzad et al. (2022) in Pakistan. On the other hand, Durani et al. (2023) revealed that financial development 

causes ecological destruction in BRICS nations. Furthermore, Ibrahim et al. (2023) proposed that the BRICS 

countries' ecosystem condition declines due to financial growth. 

Lastly, from the table, we can see that urbanization is negatively associated with the LCF variable across all 

quantiles. This outcome indicates the necessity of sustainable urban planning for the sustainable environment of 

the chosen area. Furthermore, URBA stresses ecosystems by causing forest loss, the loss of agricultural area, and 

the production of hazardous waste (Winoto and Schultink, 1996). The conclusions seem consistent with those of 

Anser et al. (2020), Azam and Khan (2016), and Gasimli et al. (2019). However, Kim (2020) discovered that 

Korea's rebound effect and high energy efficiency imply that URBA has no impact on pollution. 

 

 

 

 

 

https://link.springer.com/article/10.1007/s10668-024-04513-9#ref-CR92
https://link.springer.com/article/10.1007/s10668-024-04789-x#ref-CR13
https://link.springer.com/article/10.1007/s10668-024-04789-x#ref-CR36


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Table 6: Method of Moments Quantile Regression 

 Location Scale (1) (2) (3) (3) (4) 

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

        

LGDP -0.160*** -0.842** -0.652*** -0.721*** -0.185*** -0.434*** -0.145** 

 (0.5712) (0.2711) (0.0831) (0.2351) 0.4924) (0.6211) (0.9215) 

LGDP2 0.409*** 0.0343** 0.469*** 0.441*** 0.310*** 0.379*** 0.326** 

 (0.098) (0.618) (0.355) (0.113) (0.094) (0.333) (0.047) 

LAI 0.126** -0.00539 0.135*** 0.131** 0.126** 0.121** 0.113*** 

 (0.0517) (0.0291) (0.0638) (0.0524) (0.0515) (0.0628) (0.0964) 

LDGE -0.096*** -0.354** -0.470*** -0.766*** -0.086*** -0.406*** -0.948*** 

 (0.309) (0.174) (0.377) (0.317) (0.316) (0.373) (0.586) 

LFD 0.637*** 0.638* 0.508*** 0.042*** 0.620*** 0.195*** 0.173*** 

 (0.676) (0.381) (0.827) (0.691) (0.685) (0.818) (0.274) 

LURBA -1.124* 1.041* -0.965*** -0.094*** -0.151*** -0.213 -0.382 

 (0.109) (0.625) (0.358) (0.134) (1.123) (1.343) (2.091) 

Constant -21.7*** 1.797 -24.912*** -23.478*** -21.853*** -20.211*** -17.409** 

 (12.212) (9.950) (15.380) (13.034) (13.809) (12.901) (9.621) 

        

Observations 91 91 91 91 91 91 91 

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

 

Robustness Check 

The DKSE, AMG, and CCEMG evaluations, which were performed to confirm the reliability of our estimations, 

can be seen in Table 7. We examine the implications of GDP, GDP squared, urbanization, digital economy, 

artificial intelligence (AI), and financial development (FD) on LCF. The findings support the conclusions from the 

MM-QR regression, indicating that LCF significantly decreases as GDP per capita increases. Specifically, the 

estimators DKSE, AMG, and CCEMG show a decline in LCF of approximately 0.168%, 0.177%, and 0.210%, 

accordingly, for a 1% expansion of GDP. On the other hand, economic development over time leads to a spike in 

LCF by 0.209% (DKSE), 0.294% (AMG), and 0.139% (CCEMG), which is in line with the results of the MM-QR 

regression. Similarly, a 1% rise in AI causes a boost in LCF by 0.126% in DKSE, 0.042% in AMG, and 0.019% 

in CCEMG. This conclusion highlights the favorable consequence of AI technology on environmental 

sustainability in the G-7 region and is consistent with the MMQR analysis.  

Additionally, the DKSE, AMG, and CCEMG estimators demonstrate an upward correlation between financial 

development and LCF, with a 1% increase in FD interpreting into a rise in LCF of 0.637%, 0.629%, and 0.650% 

in the Group-7 region. These results are consistent with the results of the MMQR method. On the other hand, the 

digital economy is unfavorably related to LCF in DKSE (−0.096), AMG (−0.065), and CCEMG (−0.289), with a 

1% significance level for the first two estimations. These findings, in addition to those from the MM-QR 

regression, corroborated the idea that the adoption of the DGE degrades environmental quality. Differential effects 

of urbanization on LCF are observed in DKSE, AMG, and CCEMG. At the 5% level of significance, the 

urbanization coefficient in DKSE (-0.124) and AMG (-0.842) is detrimentally significant. Conversely, the 

CCEMG calculation displays that the coefficient is destructively significant (−0.844) at the 10% significance 



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threshold. The above findings confirm the adverse influences of urban population increase on ecosystem conditions 

in the G-7 countries and are following the MMQR technique. 

Table 7. Robustness Test 

 (1) (2) (3) 

VARIABLES DKSE AMG CCEMG 

    

LGDP -0.168*** -0.177*** -0.210* 

 (0.0522) 0.0143) (0.0255) 

LGDP2 0.209*** 0.294*** 0.139** 

 (0.5091) (0.4940) (0.5313) 

LAI 0.126*** 0.042*** 0.019** 

 (0.0585) (0.0726) (0.0662) 

LFD 0.637*** 0.629*** 0.650** 

 (0.316) (0.800) (0.620) 

LDGE -0.096*** -0.065*** -0.289 

 (0.296) (0.262) (1.470) 

LURBA -0.124** -0.842** -0.844* 

 (1.380) (15.78) (100.9) 

Constant -21.712*** -29.411** 32.317 

 (16.800) (15.611) (17.188) 

    

Observations 91 91 91 

Number of groups 7 7 7 

R-squared 0.9221 0.9023 0.9065 

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

 

D-H causality test 

The Dumitrescu & Hurlin causality assessment results are summarized in Table 8. We could reject the null 

hypothesis as the research shows unidirectional causation between LGDP and LLCF, and the corresponding p-

value is below the traditional significance thresholds. Similarly, with a p-value of 0.0197, LGDP2 Granger 

causes LLCF, suggesting that faster economic development has a major effect on LCF in the G-7 region. 

Furthermore, the p-value for LAI is 0.0017, which is under the accepted 0.05 significance threshold. We might 

therefore infer that LAI Granger causes LLCF and reject the null hypothesis. Additionally, there prevails a 

bidirectional causal relationship (p-values of 0.0064 and 0.0195, respectively) between LFD and LCF, 

demonstrating that changes in one factor also affect the other. Furthermore, as the p-values for LDGE and LCF 

are below the traditional level, suggesting that both variables Granger cause one another, there is a bidirectional 

causal connection between them. Furthermore, with p-values of 0.0268 and 0.0066, LURBA Granger causes LLCF 

and vice versa. Because the p-values are below the conventional level, we could dismiss the null hypothesis. In 

contrast, there exists no significant causal connection as displayed through p-values greater than the normal 

significance threshold for the relationships between LLCF and LGDP, LLCF and LGDP2, and LLCF and LAI. 

Thus, in these instances, we cannot rule out the null hypothesis that there is no existence of causality. 

 



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Table 8. D-H Causality Test 

Null Hypothesis W-Stat. Zbar-Stat. Prob. 

LGDP  ≠ LLCF 2.52997 1.43133 0.0123 

LLCF  ≠ LGDP 0.91328 -0.42843 0.1383 

LGDP2  ≠ LLCF 2.5697 1.47704 0.0197 

LLCF  ≠ LGDP2 0.91717 -0.42395 0.6716 

LAI  ≠ LLCF 0.66013 -0.71964 0.0017 

LLCF ≠  LAI 2.84746 1.79655 0.0724 

LFD ≠ LLCF 0.18725 -1.26362 0.0064 

LLCF  ≠LFD 2.15102 0.9954 0.0195 

LDGE  ≠ LLCF 0.39599 -1.0235 0.0061 

LLCF  ≠ LDGE 1.73064 0.51182 0.0088 

LURBA  ≠ LLCF 3.21109 2.21486 0.0268 

LLCF  ≠ LURBA 2.72999 1.66142 0.0066 

 

Conclusion and Policy Implications 

The load capacity factor (LCF) in the Group-7 countries was extensively examined in our research from 1990 to 

2022 concerning artificial intelligence (AI), economic growth, financial innovation, the digital economy, and 

urbanization. The analysis uses sophisticated econometric techniques to validate the Load Capacity Curve (LCC) 

hypothesis, and the results provide important insights into the complicated connections between financial activities 

and the well-being of the natural world. The findings of the stationarity analyses demonstrate that there are no unit 

root difficulties and that the parameters exhibit varying degrees of integration. The LCC hypothesis is confirmed 

in both the short and long term in the Group-7 region by the MMQR calculations, revealing a beneficial 

relationship between long-term economic growth, AI, financial development, and LCF. On the contrary, 

urbanization, the digital economy, and GDP growth in the near future are detrimental to LCF. It is anticipated that 

financial growth will supply the capital required to invest in sustainable innovations that will boost industrial 

operations. Similarly, strong advancements in AI coupled with green growth stimulate the establishment of fresh 

ideas and the implementation of sustainable habits by promoting competitiveness and granting access to the latest 

technologies. The DKSE, AMG, and CCEMG tests, which corroborate the outcomes of the MMQR, add to the 

results' robustness. Furthermore, significant one-way causal linkages between LGDP, LGDP2, LAI, LFD, LDGE, 

LURBA, and LLCF were found by the D-H Causality analyses. These links highlight how the G-7 region's 

environmental sustainability dynamics are influenced by shifts in economic activity, advances in artificial 

intelligence, digital technology, urbanization, and financial expansion. As a result, the analysis provides 

several policy recommendations that, when paired with green development, improvements in technology, and 

practical urban infrastructure, ought to support a healthy environment in the selected region. 

In order to tackle the U-shaped correlation between income and load capacity factor in the G-7 region, strategies 

must be developed to ensure both environmental sustainability and GDP expansion. As affluence increases, it is 

important to enforce more stringent rules on pollution and resource utilization in order to reduce the destructive 

effects on environmental sustainability. Providing subsidies and tax incentives to promote green technology and 

sustainable practices can effectively reduce environmental deterioration in the early phases of economic 



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development. Moreover, allocating resources towards education and awareness initiatives might foster sustainable 

customer behavior. With the ongoing increase in wealth, it is imperative to redirect policy toward the promotion 

of sophisticated, clean technology and renewable energy sources. This will guarantee that rising income levels 

result in enhanced environmental consequences. Promoting corporate accountability and incorporating 

sustainability indicators into financial reporting may effectively propel organizations toward adopting more 

environmentally friendly practices. Collaborating and exchanging ideas between G-7 countries can further improve 

the efficacy of these initiatives. 

To promote environmental sustainability in the Group Seven territories, policies should prioritize the integration 

of financial development and AI innovation into environmental strategies. Governments should prioritize the 

provision of financing and support for AI-driven technologies that facilitate the transition to renewable energy, 

optimize resource utilization, and improve environmental monitoring. Innovation and adoption can be stimulated 

by incentives for private sector investments in ecological AI solutions. Concurrently, financial policies should 

promote sustainable investment portfolios and green bonds, guaranteeing that financial development is consistent 

with sustainability objectives. Transparency and effectiveness will be guaranteed by the establishment of distinct 

standards and metrics for assessing the environmental impact of financial investments and AI. Fostering 

collaboration among environmental organizations, financial institutions, and tech developers, as well as promoting 

public-private partnerships, can facilitate the integration of AI-driven sustainability initiatives. Furthermore, 

policies should promote the creation of financial instruments that facilitate projects that generate substantial 

environmental benefits, thereby ensuring that financial growth is consistent with sustainable outcomes. The 

potential of AI and financial development can be leveraged by the G-7 countries to improve the green ecosystem 

through the implementation of these measures. 

Policies should concentrate on sustainable development techniques in order to mitigate the negative consequences 

of the urban population and the rise of the digital economy on the LCF in the G-7 area. Encourage the IT industry 

to adopt energy-efficient practices and technology in order to lessen its environmental impact on the digital 

economy. Incorporate green data center guidelines and incentives and encourage the use of clean power sources 

in digital infrastructure. To mitigate the ecological impact of urbanization, give priority to smart city design that 

includes green infrastructure and effective land use. Regulations should be strengthened to guarantee that stringent 

environmental impact assessments and sustainability initiatives coincide with urban growth. Invest in green 

building techniques and public transportation to lessen the total demand for natural resources. Enforce stricter 

zoning regulations to safeguard natural regions and encourage mixed-use constructions that lessen the need for 

sprawling metropolitan areas. Through the integration of these measures, the G-7 nations may alleviate the 

detrimental impacts of urbanization and the rise of the digital economy on environmental sustainability, 

guaranteeing that technological and economic progress does not come at the price of ecological well-being. 

 

Declaration  

 

Acknowledgment: N/A 

 

Funding: N/A 

 

Conflict of interest: N/A 

 

Ethics approval/declaration: N/A 

 

Consent to participate: N/A 

 



Global Sustainability Research 

Global Scientific Research  44 
 

Consent for publication: N/A 

 

Data availability: Available on request. 

Author’s contribution: Mohammad Ridwan conceptualized the study and supervised the research. 

Shewly Bala conducted the data analysis and contributed to writing. Sarder Abdulla Al Shiam and Afsana 

Akhter assisted with data collection and literature review. Md Mahdi Hasan, Md Asrafuzzaman, Shake 

Ibna Abir, and Shaharina Shoha contributed to data processing and analysis. Robeena Bibi provided 

editorial support and helped finalize the manuscript. 

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