







































Global Sustainability Research                                                     ISSN: 2833-986X 
https://doi.org/10.56556/gssr.v3i3.972 

                                                                  

 
 

Global Scientific Research            54 
 

RESEARCH ARTICLE  

Exploring the LCC Hypothesis in the Nordic Region: The Role of AI Innovation, 

Environmental Taxes, and Financial Accessibility via Panel ARDL 

Md Sibbir Hossain1, Mohammad Ridwan2*, Afsana Akhter2, Md Boktiar Nayeem3,  

M Tazwar Hossain Choudhury3, Md Asrafuzzaman4, Shaharina Shoha5,  

Shake Ibna Abir5, Sumaira6 

 

1Department of Computer Science, The City College of New York, Convent Ave, New York, NY 10031, USA  
2Department of Economics, Noakhali Science and Technology University, Sonapur, Noakhali-3814, Bangladesh  
3College of Graduate and Professional Studies, Trine University, University Ave, Angola, IN 46703 
4Department of Management -Business Analytics, St Francis College, USA  
5Department of Mathematics, Western Kentucky University, Bowling Green, KY, USA 
6College of economics and Management, Zhejiang Normal University, Zhejiang China 

 

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

Received: 11 August, 2024, Accepted: 03 September, 2024, Published: 03 September, 2024 

 

Abstract 

This study investigates the impact of artificial intelligence (AI) innovation on environmental sustainability in the 

Nordic region. Additionally, it tests the Load Capacity Curve (LCC) hypothesis by incorporating factors such as 

financial accessibility, environmental tax, and urbanization, using data spanning from 1990 to 2020. The 

methodology includes the Cross-Sectional Dependence test and the slope homogeneity test, revealing issues of 

heterogeneity and cross-sectional dependence. Furthermore, first and second-generation panel unit root assessments 

indicate that the variables are free from unit root problems. Panel Cointegration tests confirm that the variables are 

cointegrated in the long run. To analyze both short-run and long-run relationships, the study employs the Panel 

Autoregressive Distributed Lag (ARDL) model. The results from the Panel ARDL model support the LCC 

hypothesis in the Nordic region, showing a U-shaped relationship between income and load capacity factor. 

Moreover, AI innovation and environmental tax significantly and positively contribute to environmental 

sustainability in both the short and long run. In contrast, higher financial accessibility and urbanization degrade 

environmental sustainability over these timeframes. To validate the robustness of the Panel ARDL estimations, the 

study also uses Fully Modified OLS, Dynamic OLS, and Fixed Effects OLS approaches, all of which corroborate 

the ARDL findings. The study employs the D-H causality test to explore causal relationships among the variables. 

The test results reveal a unidirectional causal relationship between income and AI innovation to the load capacity 

factor and a bidirectional causal relationship between financial accessibility and the load capacity factor, as well as 

between urbanization and the load capacity factor. However, no causal relationship is found between environmental 

tax and the load capacity factor. 

 

Keywords: Artificial Intelligence; Environmental Tax; Financial Accessibility; Load Capacity Factor; Nordic 

Region



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Introduction 

Severe environmental degradation is increasingly in line with not just climate change but also other human activities 

and catastrophes related to the air, water, and soil (Ahmad et al., 2020; Alola et al., 2020; Islam et al.2023). The 

developed and developing worlds have made it a priority to prevent temperature rise and damage to the planet 

(Apergis et al., 2023; Polcyn et al.2023; Rana et al.2024). Furthermore, rising global GHG emission levels are 

mostly caused by variables, including the growth of the urban population, industrialization, financial accessibility, 

and monetary development (Asongu et al., 2020; Wanof,2023). With its extensive coastline, dense forests, 

mountains, rivers, valleys, and Arctic tundra in the north and warm regions in the south, Nordic nature is 

unparalleled. A diversified society, ecosystem, way of life, and health have all been impacted by the threat that 

climate change poses to the Nordic region (Baral, 2024). Norway is dedicated to a sustainable environment and 

climate policy, and it serves as a worldwide example for the mitigation of global warming (Anker, 2018). With 

around 31% of the gas imported by the EU coming from Norway, it is the most significant natural gas supplier 

among Western European nations. Therefore, the main driver of Norway's GDP growth is the oil and gas sector 

(Zakeri et al., 2022). In light of the foregoing, the initial objective of the research is to analyze the effects of GDP 

growth, environmental taxes, financial accessibility, urbanization, and artificial intelligence (AI) innovation on load 

capacity factor (LCF) connections in the Nordic region. The Nordic countries were chosen because they have 

become well-known worldwide for their dedication to sustainable growth, environmental quality, and taking the 

lead on global climate change mitigation (Owusu et al., 2024; Raihan et al.2022a). For example, Norway is now 

the most sustainable country in the world and plans to become carbon-neutral by 2030. According to projections 

from the International Hydropower Association, hydropower accounts for 95% of the country's power generation 

(Malka et al., 2023). The Nordic countries have made great progress, but climate change still presents problems, 

such as higher rainfall, water damage, and rising sea levels (Depren et al., 2023). Moreover, Norway's CO2 

emissions, with 49.3 mt, placed it tenth globally. In 2018, Norway produced 55% of its energy from fossil 

fuels (Owusu et al., 2024). 

The four Nordic nations (Denmark, Sweden, Finland, and Norway) are the first to employ renewable energies 

extensively, have excellent environmental quality, and have enacted environmental taxes (He et al., 2019). The first 

nation in the EU to genuinely enact green tax measures is Denmark. The Danish economy has expanded by 78% 

over the last 30 years, according to official data, while energy consumption has essentially stayed the same (OECD, 

2019). The four Nordic nations' combined carbon emissions make up roughly 0.96% of all carbon emissions 

worldwide (BP, 2019). Moreover, Denmark is at the forefront of the green transition. Since 1996, it has effectively 

cut its CO2 emissions by more than half. Carlini et al. (2023) report that 47% of the country's 2019 electrical 

generation came from wind power. Because green methods of economic growth are significantly superior to means 

of progress that degrade the environment, the five sovereign Nordic countries have a moderately better level of 

financial stability than China, the United States, and India (Sharif et al., 2023). Finland is thought to be among the 

leaders in environmental issues (Wurzel et al., 2020). The success of Finland's progressive environmental policies 

has historically been attributed to the early participation of stakeholders in policy processes (Koskimaa et al., 2021). 

The policy tool used to reduce the usage of energy, which varies depending on the economy and business, is 

environmental taxes (Bashir et al., 2021). Several climate change targets, including lower pollution, water 

disposal, and waste, etc., can be achieved through the execution of the environmental tax to ensure financial and 

ecosystem effectiveness and clean energy utilization (Bakirtas and Akpolat,2018; Baltagi et al.,2016). The 

discussion about the financial progress of the E-7, India, and BRICST countries, as documented by Gao et al. 

(2024), Raihan and Voumik (2023), and Durani et al. (2023), respectively. According to Chen et al. (2019), progress 

in finance is significant since it can improve the financial system's economic effectiveness in a nation. More energy 

use is expected to come from faster economic expansion, which may have detrimental consequences on the 



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environment. However, technological innovation can worsen the natural world and boost carbon emissions (Du et 

al., 2019; Ali et al., 2023). 

The current research considers the trends and main research areas of AI innovation, financial accessibility, 

environmental tax, urbanization, and economic growth in the LCF of the Nordic territories and offers a structure 

for upcoming endeavors to investigate. The ecological footprint measures the extent to which human demands for 

nature exceed nature's ability to provide those requirements (Wackernagel and Kitzes 2008). This specific tool 

simply illustrates how human need for natural resources has led to ecosystem deterioration (Caglar et al.,2023). 

Siche et al. (2010) suggested calculating the LCF as the ratio of BIO to EFP to remedy such a deficit. Thus, LCF 

tackles both sides of the debate: the supply of nature and the demand from humans (Akhayere et al., 2023; Arif et 

al.2024). An ecosystem can support human demand if its LCF score is one or greater; if it is less than one, human 

demand cannot be met by the ecosystem (Kartal et al., 2023; Ridwan, 2023; Shiam et al.2024). This study allows 

us to address the following objectives: What effects do taxation on the environment, financial accessibility, and AI 

innovation have on LCF in Nordic countries? How do GDP and urbanization affect the environmental sustainability 

of the chosen area? Our study is significant as it can determine whether the innovative green solutions and vibrant 

taxation systems that have been suggested can innovatively foster a green environment and lessen the negative 

effects of monetary availability, economic growth, and rising urban populations in the nations that have been 

chosen. Analyzing the LCF drivers in the context of the Nordic region will add a great deal of understanding to the 

body of expertise and provide researchers studying the topic with fresh perspectives. The uneven impacts in the 

selected areas may be examined by using a unique panel ARDL approach that considers quite recent variables such 

as the environmental tax, AI innovation, and financial accessibility on LCF. More sophisticated procedures, 

including both first and second-generation techniques, are utilized in this empirical study to address the obstacles 

of heterogeneity and the CSD problem. The FMOLS, DOLS, and FE-OLS assessments were also adopted in our 

study to verify robustness. Several noteworthy empirical findings demonstrated the necessity of appropriate 

legislation to facilitate the effective use of the latest innovations, provide funding, and enhance policy-level 

approaches for ecological protection. 

The remaining portions of this work are organized this way: The literature is analyzed in the "Literature review" 

part. The data, conceptual framework, and methods are presented in the section under "Data, Model, and 

Methodology." The subsection titled "Empirical results and discussion" presents the empirical outcomes and 

discourse, while the final part titled "Conclusions and policy implications" concludes with strategy proposals. 

 

Literature Review 

Numerous investigations have explored the (LCC) theory, the Panel ARDL model as well as the connections 

between globalization, economic progress, and the expansion of financial institutions. Because load capacity factor 

(LCF) is still a relatively fresh concept, there is still a lack of research on it in the Nordic region. Furthermore, not 

much research has been done on recent variables, including environmental taxes, financial accessibility, and 

innovations in artificial intelligence. Nevertheless, earlier research has influenced the technique and variable choice 

for these studies. A few of these investigations will be covered in this section. 

Within a conventional development process, adverse environmental effects, destruction of ecosystems, and 

excessive use of assets drive growth in the economy (Khan et al. 2019a; Ridzuan et al.2023). This is an undesirable 

development mode since it is destructive to the long-term survival of humans. Using the GMM technique, Latiff 

and Faridi (2023) conducted a study in 48 Asian nations, analyzing data from 1996 to 2020. The results showed 

that in the selected nations, environmental degradation is a result of economic expansion. From 1990 to 2018, Caglar 

and Askin (2023) explored the connections between GDP and LCF in the top 10 CIP areas. According to the study, 

growing GDP degraded the ecosystem and reduced LCF. Similarly, several studies by Akadiri et al. (2022) in India, 



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Addai et al. (2023) within 9 Eastern European economies, and Shah et al. (2021) in South Asian countries also align 

with this conclusion. Ali et al. (2023) found an unexpected long-term negative association (-0.270) between GDP 

and the LCF in Pakistan. Dai et al. (2023) evaluated environmental quality and the relationship between green 

energy (GEN) and ecological quality in ASEAN countries using the LCF. They found that environmental quality 

decreases with economic growth. Moreover, empirical studies (Sinha and Shahbaz, 2017; Allard et al., 2018) 

discovered that the connection between ecosystem quality and GDP expansion was inconsistent, inadequate as well 

as highly reliant on the substitutes selected, the statistical approach employed, and the volume of data. However, 

Usman et al. (2024) examined a complex link between GDP growth and LCF, with early gains in LCF associated 

with GDP growth and later depreciation after a certain point in time in China. Moreover, Jin et al. (2023) found that 

GDP growth in Germany had a U-shaped relationship with LCF.  

The academic community has become increasingly aware of AI's rapid growth and has focused research efforts on 

its socioeconomic implications (Zador et al., 2023; Fosso Wamba, 2022). Certain scholars have noted the possible 

effects of AI on ecosystems (John et al., 2022). According to Wang et al. (2023), AI significantly reduces ecological 

footprints. Furthermore, there is a growing tendency for AI to have a marginal impact on lowering the ecological 

footprint (EFP) as globalization gets deeper. Moreover, AI has shown a greater level of promise in the areas of 

environmental monitoring and renewable energy (Ahmad et al., 2021; Ye et al., 2020). As per Vinuesa et al. (2020), 

artificial intelligence (AI) could add considerably to the majority of the 17 goals and 169 targets in the United 

Nations "2030 Agenda for Sustainable Development." Artificial intelligence actively contributed to the mitigation 

of CO2 pollution in seven emerging Asian nations between 1990 and 2020 through the use of NARDL and 

asymmetric panel causality methodology, as reported by Rasheed et al. (2024). Moreover, a major factor in the rise 

in carbon footprints is the utilization of IT. In a similar vein, Zhao et al. (2023) concurred with this scenario and 

concluded that, in 30 Chinese provinces, AI could reduce pollution density significantly—by 6.63% for every 10% 

increase in utilization. According to Chen et al. (2022), AI may reorganize production factors, give traditional 

industries information drivers to upgrade, encourage rapid growth of new sectors, increase efficiency in energy use 

through upgrades to business structures, and boost the quality of the natural world. 

One effective government policy tool for raising LCF and lowering pollution levels globally is the imposition of 

environmental levies. Green taxes are a sustainable addition to the government's economic policy and a means of 

preventing pollutants from spreading into the ecosystem (Hieu, 2022). Governmental entities use stringent 

alterations, including environmental taxes, to minimize carbon-intensive activities to tackle pressing ecological 

issues (Ulucak et al., 2020). Using the QQR approach, Depren et al. (2023) executed a study in the Nordic countries 

between 1994 and 2020. The conclusions suggested that the consequence of ecological taxes on CO2 emissions 

was multifaceted. Similarly, Javed et al. (2023) demonstrated that one of the main factors lowering ecosystem 

quality is environmental taxation. Esen and Dundar (2021) highlighted the possible effectiveness of environmental 

levies in managing environmental plans using data from Turkey using the FMOLS and DOLS methodologies. 

Bozatli and Akca (2024) showed that the application of the ARDL technique in the Netherlands' environmental tax 

and conservation investments are both efficient means of guaranteeing environmental sustainability. Moreover, 

adopting the MMQR approach, Jahangir et al. (2024) examined the influence of environmental taxes on LCF in the 

top ten SDG countries between 1994 and 2018. The investigation outcomes demonstrate that environmental taxes 

have an inverse and substantial impact on LCF within the nations. Comparably, between 1994 and 2017, 

Degirmenci and Aydin (2021) performed research in several African states. The results indicate that green 

taxes exacerbated conservation efforts in South Africa and boosted ecological damage in Cameroon, Ivory Coast, 

and Mali. Numerous researchers, including Mehboob et al.(2024) in the top 5 emitting countries, Sharif et al.(2023) 

in Nordic countries,  Dogan et al. (2022) in 25 environmentally friendly countries, and Niu et al. (2028) in China 

concurred with this conclusion, stating that emission a decline improves the environment. 



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The concept of financial accessibility (FA) is gaining recognition as complex, and several indicators are helpful 

when assessing it (Tian and Li, 2024). Financial accessibility is the way of enabling financial goods and the 

availability of services that are equal to all viable individuals and organizations (Menkeh 2021; Zaidi et al. 2021). 

It includes financial accounts, loans for homes, credit cards, trade facilities, ATMs, savings, and remittances (United 

Nations 2006). The empirical literature (Fakher, 2019; Zaidi et al., 2019) attests to the fact that the growth of a 

profitable sector can mitigate environmental damage. Islam (2022) examined how five South Asian economies' 

environments were affected by financial expansion between 1980 and 2018. The improvement of finance is 

destructively linked with the deterioration of ecosystems due to the linear relationship between finance and CO2 

emissions, which does not diminish as financial inclusion increases. Research has shown that FA increases carbon 

emissions over time in Sub-Saharan African countries. This highlights the necessity for legislative frameworks that 

connect financial inclusion programs with restrictions on the environment (Ogede and Tiamiyu, 2023). Raihan et 

al.(2024a) conducted a study in the G-7 area by using the ARDL framework and found that FA harms the ecosystem. 

However, Hussain et al. (2024) found that the integration has a short-term negative impact on carbon emissions and 

a long-term positive effect using a balanced panel data set of 26 Asian countries. On the other hand, in China, Feng 

et al. (2022) and Shahbaz et al. (2022) discovered that financial accessibility improves the condition of the natural 

world. Similarly, Chaudhry et al. (2022) investigated the fluctuating effects of financial integration on carbon 

emissions in the OECD economies between 2004 and 2017 using the DCCE approach. Greater financial inclusion 

has been shown to have both short- and long-term effects on lowering CO2 emissions.  

Due to the substantial shift in population from rural to urban areas, URB's rapid growth, which has been reliant on 

industrial growth, increased demand for commodities and needed higher output levels (Caglar et al.,2023). 

Urbanization affects the LCF in a country that grows swiftly in several ways. Balsalobre-Lorente et al. (2021) 

determined that URBA enhanced ecological well-being, with a 1% increase in URBA decreasing emission levels 

by 0.389 percent in the BRICS region. Pata et al. (2023) observed that URBA reduced LCF in the USA after running 

an ARDL model. Similarly, using DKSE, Alola et al. (2023) analyzed various environmental indicators for the 

panel of Sweden, Denmark, Finland, Norway, and Finland. Their findings demonstrate that rising emissions of 

greenhouse gases from urbanization worsen environmental quality. On the other hand, Gang et al. (2023) evaluated 

how the MENA area's urbanization affected the environment between 2004 and 2019. Urbanization has contributed 

significantly to ecological sustainability both in the short and long terms, according to research done using the CS-

ARDL approach. The ARDL methodology was applied in the publication by Ali et al. (2017) to find a significant 

detrimental consequence of urbanization on Singapore's pollution of carbon. They revealed that the degradation of 

the environment is not caused by it. Moreover, Wang et al. (2021) analyzed that urbanization lowers carbon 

emissions using the ARDL methodology; however, the impact is negligible in OECD countries. In order to lower 

carbon emissions, they advise promoting the urbanization process and utilizing its scale effect. In the same way, 

Khan et al. (2023) showed through the NARDL method that urbanization benefits India's environment in the long 

run. Surprisingly, Xu et al. (2022) explored the connection between urbanization and LCF in Brazil between 1970 

and 2017, concluding that URBA does not influence LCF. 

The majority of the literature now in publication focuses on research related to the environment's demand side, and 

the beneficial and detrimental impacts of explanatory indicators on ecological footprint have been documented. 

Furthermore, a procedure that prioritizes the LCF and considers the supply aspect of the environment is presently 

in place. A research opportunity has arisen because there are currently no studies looking at the possible implications 

of green taxes, financial accessibility, and AI innovation on the LCF for Nordic economies. In addition, innovation 

and environmental taxes can improve sustainable development by optimizing the utilization of resources, cutting 

emissions, leveraging contemporary technologies, and motivating greener lifestyles. A thorough examination of the 

competitive advances achieved in economies is required by taking into account the most recent timeframes and new 



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metrics. Thus, by employing cutting-edge techniques like ARDL methodologies to assess the validity of the LCC 

hypothesis in Nordic nations, this study contributes to the growing body of literature. 

Methodology 

Data and Variables 

The World Bank Development Indicators (WDI), Global Footprint Network (GFN), Global Financial Development 

(GFD), and Our World in Statistics are the sources of the statistics, which cover the years 1990 to 2022. The factors 

used in the study are listed in Table 1. The GFN provides the LCF data, which is measured as Gha per person. 

Urbanization, GDP squared, and GDP per capita information from WDI. The number of annual patent applications 

related to AI is gathered from Our World in Data and is used to estimate AI innovation. The ratio of deposit money 

banks' assets to GDP (%) is used as an indication for environmental tax (ENT), and data on ENT and financial 

accessibility comes from GFD. The table below presents the logarithmic expression of each factor. 

 

Table 1. Description and Sources of Variables 

Variables Description Logarithmic Form Unit of 

Measurement 

Source 

LCF Load Capacity 

Factor  

LLCF Gha per person GFN 

LGDP Gross Domestic 

Product  

LGDP GDP per capita 

(Current US$) 

WDI 

LGDP2 Gross Domestic 

Product  

LGDP2 GDP per capita 

(Current US$) 

WDI 

LAI AI Innovation LAI Annual patent 

applications 

related to AI 

Our World in data 

LENT Environmental Tax LENT Deposit money 

banks assets to 

GDP (%) 

Global Financial 

Development 

LFA Financial 

Accessibility  

LFA Stock market 

capitalization to 

GDP (%) 

Global Financial 

Development 

LURBA Urbanization  LURBA Urban Population 

(% of total 

population) 

WDI 

 

Theoretical Framework 

The load capacity curve (LCC) provides valuable insights into the complex links between environmental 

sustainability, financial stability, and the development of people, and it is a vital instrument in ecological education 

(Ridwan et al.2023). This curve is important because it demonstrates if the environment's capability of recovering 

its resources (biocapacity) and how it utilizes human resources (ecological footprint) are balanced or not. The LCC 

is thought to have a U-shaped relationship, with GDP and GDP square functioning as the primary contributors. A 

U-shaped curve relationship has been suggested in the context of LCF (Pata and Tanriover, 2023; Pata and Ertugrul, 

2023; Urbee et al.2024), indicating that there are different patterns in the impact of GDP and GDP square on 

environmental quality. According to Wu et al. (2024), there is initially an inverse association between GDP growth 



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and resource exploitation and energy use. Several researchers (Apergis et al., 2023; Aydin & Degirmenci, 2023; 

Shahzad et al., 2024; Pata & Samour, 2022) examined the LCC hypothesis in the literature. 

For LCC theory, we can consider the following equation: 

load Capacity Factor = f(GDP, GDP2, Yt)                      (1) 

Here, wealth is expressed by GDP and GDP squared in equation (1), while other factors influencing the load 

capacity factor are shown by Yt. To generate a greater idea of the factors affecting the LCF, equation (2) integrates 

other noteworthy variables, including artificial intelligence innovation, economic expansion, financial accessibility, 

environmental taxation, and urbanization. 

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

LCF stands for the load capacity factor in equation (2); AI innovation is represented by AI; FA refers to the 

environmental tax; FA represents financial accessibility; and URBA means urbanization. The econometric 

explanation of equation (3) is given above. 

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

The variables' logarithmic values are shown in equation (4). This conversion improves interpretation and makes 

statistical results conceivable by breaking down complex interactions into simpler linear forms. 

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

Empirical Method 

In the Nordic region, our investigation used the ARDL framework for data assessment to figure out the link across 

LCF and variables such as GDP, AI, ENT, FA, and URBA. Moreover, we utilized the FMOLS, DOLS, and FE-

OLS techniques to guarantee robustness. To ensure stationarity, the CSD, slope homogeneity examination, and unit 

root assessments (IPS, CIPS, and CADF) were conducted at the start of the inquiry. The estimates for the short- and 

long-term ARDL were then finished. The causal link among the chosen factors was then demonstrated using the D-

H causality analysis. After a difficult evaluation process, we were able to determine which econometric framework 

was the most precise and efficient. 

Cross-Sectional Dependence Test 

We first confirm that panel data indicate cross-sectional dependency (CSD) before starting our econometric 

analysis. For projections to be trustworthy and observations to be reliable, the CSD phenomenon must be addressed 

(Grossman and Krueger, 1995). Due to greater financial integration and the removal of other obstacles, CSD in 

panel data econometrics is going to increase (Tufail et al., 2021; Voumik et al.2023a; Voumik et al.2023b ). If we 

overlook the issue and think that cross-sections are independent, we might get reliable and consistent results 

(Westerlund & Edgerton, 2007). CSD is investigated in this study using large panel data econometrics with weak 

exogenous CSD (Pesaran, 2015). Equation (5) provides a quick explanation of the typical equation used in CSD 

testing below. 



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𝐶𝑆𝐷 = √
2𝑇

𝑁(𝑁−1)𝑁
(∑ ∑ 𝐶𝑜𝑟𝑟𝑖,𝑡

̂𝑁
𝐾=𝑖+1

𝑁−1
𝑖=1 )……………………… (5) 

Slope Homogeneity test 

Slope heterogeneity must be considered when evaluating panel data (Voumik et al.2023c). The experiments on 

slope homogeneity by Pesaran and Yamagata (2008) are then carried out. The test results are determined by 

accounting for each participant's weighted slope. The following is how Equation (6) depicts the slope heterogeneity:  

∆ ̌ = √𝑁 (
𝑁−1𝑆%−𝑘

√2𝑘
) and ∆̌𝑎𝑑𝑗=  √𝑁 (

𝑁−1𝑆%−𝑘

√
2𝑘(𝑇−𝑘−1)

𝑇+1

)………………………………………(6) 

Panel Unit root test 

Traditional unit root analyses may yield inaccurate results due to CSD issues and slope variability (Dogan & Seker, 

2016). Initially, the researchers employed the first-generation IPS test introduced by Im et al. (2003). The first 

generation of panel unit root testing does not take heterogeneity, CSD effects, or over-rejection of null hypotheses 

into account (Choi, 2001). For this reason, second-generation tests like CIPS and CADF, introduced by Pesaran 

(2007), were utilized in our study. The IPS unit root test is given below: 

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

In contemporary literature, the ability of CIPS to regulate heterogeneity and CSD has gained popularity (Akther et 

al.,2024). The CIPS test is conducted using Equation (8): 

 

𝐶𝐼𝑃𝑆 =  
1

𝑁
∑ 𝑡1(𝑁, 𝑇)𝑁

𝑡=1 ……………………………… (8) 

The CADF test has a strong relationship with the CIPS test. The alternative in the CADF assessment is some cross-

section units in the panel do not have a unit root, while the null hypothesis is each cross-section unit in the panel 

includes a unit root (Hossain et al.,2024). Equation (9) provides the following method for computing the CADF: 

∆𝑌𝑖𝑡 =  𝜑𝑖 +  𝛿𝑖𝑌𝑖𝑡−1 + 𝜑𝑖�̅�𝑡−1 + ∑ 𝜑𝑖𝑗�̅�𝑡−1

𝑚

𝑗=1
+ ∑ 𝜌𝑖𝑗

𝑚

𝑗=1
∆𝑌𝑖,𝑡−1 + 휀𝑖𝑡 … … … (9) 

Here, �̅�𝑡−1 and ∆𝑌𝑖,𝑡−1 represent the mean values of the cross-sectional analysis for both the first difference and lag. 

Panel Cointegration Test 

Investigating the existence of long-term relationships across the factors under study is crucial once the sequence of 

integration has been determined (Larsson et al., 2001). The Pedroni (1999, 2004) heterogeneous panel cointegration 

assessment is appropriate for CSD with distinctive particular results. It was utilized in the research; unlike Kao 

(1999), this method allows the AR coefficients to differ throughout panels. Seven statistics are offered by this test 

to evaluate the cointegration properties; three of the statistics are based on the between-dimension, and four are 

based on the within-dimension. It is carried out in this manner: 

𝑠ℎ𝑖𝑡 = 𝜆0 +  𝛼𝑖𝑡 + 𝛿1𝑖𝑏𝑘𝑑𝑖𝑡 + 𝛾2𝑖𝑖𝑏𝑖𝑡 + 𝛽3𝑖𝑖ℎ𝑟𝑖𝑡 + 휀𝑖𝑡…………… (10) 



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

reveal how far the long-run association deviates from expectations. 

Panel ARDL Technique 

The study employed the ARDL model, initially introduced by Pesaran et al. (2001), as an innovative way to assess 

the short- and long-term connections among the model's parameters. This approach allows for cross-sectional 

heterogeneity, which is necessary to capture the distinct technological and economic environments of the Nordic 

nations. It takes two phases to put the ARDL approach into practice. The first step is to adopt the F test to determine 

whether there is a long-term link between the pertinent variables in the presence of an error correction. After 

confirming that the F tests from the first process fall within acceptable bounds, the ARDL's second step involves 

estimating the long-run relations' coefficients (Hazmi et al.,2024). 

 

𝑙𝑛𝐿𝐶𝐹𝑡 = 𝜑0 + 𝜑1lnLCFt−1 + 𝜑2𝑙𝑛𝐺𝐷𝑃𝑡−1 + 𝜑3𝑙𝑛𝐺𝐷𝑃^2𝑡−1 + 𝜑4𝑙𝑛𝐴𝐼𝑡−1 + 𝜑6𝑙𝑛𝐸𝑁𝑇𝑡−1 + 𝜑7𝑙𝑛𝐹𝐴𝑡−1

+ 𝜑8𝑙𝑛𝑈𝑅𝐵𝐴𝑡−1 + ∑ ℵ1 ∆𝑙𝑛𝐿𝐶𝐹t−i

𝑤

𝑖=1

+ ∑ ℵ2 ∆lnGDPt−i

𝑤

𝑖=1

+ ∑ ℵ3∆lnGDP^2t−i

𝑤

𝑖=1

+ ∑ ℵ4 ∆

𝑤

𝑖=1

𝑙𝑛𝐴𝐼𝑡−𝑖 + ∑ ℵ5∆lnENTt−i

𝑤

𝑖=1

+ ∑ ℵ6∆lnFAt−i

𝑤

𝑖=1

+ ∑ ℵ7∆lnURBAt−i

𝑤

𝑖=1

 

+ 휀𝑡                                 … … … … … … … … (11) 

 

The ARDL model has the ability to evaluate both short-run and long-run factors at the same time, which is one of 

its primary benefits. Furthermore, I (0) or I (1), or whatever frictionally integrated time series variable these 

variables tend to be, might be applied with this framework. To guarantee an appropriate regression or ARDL 

procedure consequence, appropriate variables must be used (Raihan et al.,2024b). The null hypothesis, which claims 

that there is no cointegration, is compared with the evidence in favor of cointegration (the alternative hypothesis). 

Equations 12 and 13 represent the null hypothesis (H0) and the alternative hypothesis (H1). 

 

𝐻0 = ℵ1 = ℵ2 = ℵ3 = ℵ4 = ℵ5 = ℵ6                                               (12) 

𝐻1 = ℵ1 ≠ ℵ2 ≠ ℵ3 ≠ ℵ4 ≠ ℵ5 ≠ ℵ6                                                   (13) 

After long-term connections are established, the error correction model (ECM), developed by Engle and Granger 

(1987), is utilized to evaluate the Error Correction Term (ECT) and short-term correlations.  

 

𝑙𝑛𝐿𝐶𝐹𝑡 = ℵ0 +  ∑  ℵ1∆lnLCFt−i

𝑤

𝑖=1

  + ∑ ℵ2∆lnGDPt−i

𝑤

𝑖=1

+  ∑ ℵ3∆lnGDP^2t−i                           

𝑤

𝑖=1

+ ∑ ℵ4∆

𝑤

𝑖=1

𝑙𝑛𝐴𝐼𝑡−𝑖 + ∑ ℵ6∆lnENTt−i

𝑤

𝑖=1

+ ∑ ℵ6∆lnFAt−i

𝑤

𝑖=1

+ ∑ ℵ6∆lnURBAt−i

𝑤

𝑖=1

+ ∀𝐸𝐶𝑇𝑡−𝑖+휀𝑡                                     (14)  

 



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Where ∀ is the rate of adaptation? 

Robustness Check (FMOLS, DOLS, FE-OLS) 

First, the Fully Modified Ordinary Least Square (FM-OLS) was introduced by Phillips and Hansen (1990), and then 

the Dynamic Ordinary Least Square, established by Stock and Watson (1993), was employed. FM-OLS modifies 

the OLS to reduce serial correlation effects and endogeneity in the regressors resulting from a co-integrating 

connection. The FMOLS method uses a non-parametric strategy to manage the autocorrelation and endogeneity 

problems (Adebayo et al., 2022). In contrast, the DOLS test takes into account both the timing of differences and 

their eventual leads and delays through explanatory factors to remove serial correlation in the covariance matrix of 

errors used to calculate standard deviations (Raihan et al., 2023a). The authors further use the FE-OLS estimator 

expanded with Driscoll and Kraay's (1998) standard errors, which have been proven more robust.  

D-H Causality Test 

Lastly, this research employs the Dumitrescu and Hurlin (2012) causality assessment to confirm the short-term link 

between variables, which is essential regarding policymaking. This test may be used to predict cross-sectional 

independence and dependency scenarios. Granger causality tests on the conventional panel show that the 

homogenous null hypothesis is the reason for any causal relationship found in a subgroup of the variable due to a 

deficiency of cross-sectional data (Ahmed et al., 2022). There are certain benefits to the Dumitrescu and Hurlin 

(2012) test procedure. Firstly, this causality test's standardized, average Wald statistics have a conventional normal 

asymptotic distribution and are easy to compute. Furthermore, no specific panel estimations are needed for the 

statistics. This method can be versatile and helpful for obtaining reliable outcomes during CD as it can calculate 

both N > T and T > N samples (Ahmed and Le, 2021).  

Results and Discussion 

Summary Statistics 

Descriptive statistics and the findings of several analyses that were performed to determine whether the data were 

normal are given in Table 2. The table shows that LCF has the lowest mean value of 0.01950 while GDP squared 

has the highest mean value of 118.2038. In addition, all of the variables' estimated standard deviations are extremely 

low, indicating that the data points are mostly centered around the mean and exhibit limited cyclical 

variation. The data is skewed to the left as the skewness statistics for most of the factors are negative. Both the 

probability values and the Jarque–Bera analysis revealed that each of the components had a normal distribution. 

The investigation's findings additionally showed that no variable had a notable departure from the associated mean 

values.  

 

Table 2. Summary statistics of variables 

Statistic LLCF LGDP LGDP2 LAI LENT LFA LURBA 

Mean 0.019509 10.86771 118.2038 3.102615 5.287321 3.909362 4.45272 

Median 0.234705 10.87999 118.3742 3.135494 8.523018 3.862088 4.447006 

Maximum 0.860973 11.54785 133.3528 3.89182 9.311575 4.712179 4.542699 

Minimum -0.945215 10.10012 102.0124 1.791759 -1.134136 3.301475 4.335695 

Std. Dev. 0.582664 0.312364 6.773281 0.502838 4.407573 0.363945 0.053723 



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Skewness -0.244537 -0.179975 -0.092862 -0.529248 -0.376236 0.464751 0.045105 

Kurtosis 1.416313 3.055801 3.038905 2.47716 1.171219 2.141118 2.550493 

Jarque-Bera 12.59159 0.608104 0.165031 6.388144 17.92383 7.340905 0.963392 

Probability 0.001844 0.737823 0.920797 0.041005 0.000128 0.025465 0.617735 

Sum 2.146037 1195.448 13002.42 341.2877 581.6054 430.028 489.7992 

Sum Sq. Dev. 37.00517 10.63526 5000.63 27.56027 2117.51 14.43768 0.314587 

Observations 110 110 110 110 110 110 110 

 

Results of Cross-Sectional Dependence Test 

A CSD test is the primary step for analyzing econometric panel data. The Pesaran CD test outcomes are shown in 

Table 3. All of the CSD statistics values are highly significant at the 1% significance threshold, and for each 

variable, the value is less than 0.05. The results demonstrate that the null hypothesis, according to which no CSD 

prevails between countries, is rejected for each variable. This suggests that an unforeseen incident that takes place 

in one of the sample nations may also have an impact on the countries that follow. 

 

Table 3. Results of CSD test 

Variables CD-Statistics P-Value 

LLCF 3.48*** 0.003 

LGDP 11.03*** 0.000 

LGDP2 12.97*** 0.000 

LAI 6.11*** 0.000 

LENT 5.21*** 0.000 

LFA 6.83*** 0.000 

LURBA 14.18*** 0.000 

 

Results of the Slope Homogeneity Test 

The slope heterogeneity (SH) examination results are summarized in Table 4. Based on the determined p-values of 

0.002 and 0.000, the null hypothesis, which asserts that the slope coefficients are homogeneous and is rejected at 

the 1% significance level. These p-values illustrate that distinct variables have different coefficients, which is 

sufficient evidence to reject the homogeneity hypothesis. 

 

Table 4. Results of Slope Homogeneity test 

Slope Homogeneity 

test 
Δ statistic P-value 

Δ̌ test 3.171*** 0.002 

Δ̌𝑎𝑑𝑗 test 3.975*** 0.000 

 

 



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 Results of panel unit root test 

The IPS test findings demonstrate that only LAI is stationary in its level form at a 1% significance level, while the 

remaining variables are significant and stationary at the first difference, ensuring consistency in the panel data 

analysis process. At a 5% significance level, the outcomes of the CIPS test likewise reveal that LAI is the only 

variable that is stationary at the level form (I(0)). The remaining variables (LLCF, LGDP, LGDPSQ, LENT, LFA, 

and LURBA) are stable at the first difference (I(1)) at a significance threshold of 1%. Comparably, the CADF unit 

root analysis verifies that, at the 1% significance level, only LAI is stationary at the level form (I(0)), whereas the 

others are stationary at the first difference (I(1)). Furthermore, every factor stays significant at the level of 1% upon 

differencing, except LGDP, which maintains significance at the 5% level. These conclusions indicate significant 

cointegration and indicate that the variables do not have a unit root problem. 

 

Table 5. Results of Panel Unit root test 

Variables IPS  CIPS  CADF  Decision 

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

LLCF -2.038 -6.811*** -2.085 -5.331*** -1.813 -4.475*** I(1) 

LGDP -2.096 -3.452*** -2.035 -3.469*** -0.155 -4.355** I(1) 

LGDP2 -2.062 -3.482*** -2.001 -3.456*** -1.132 -5.651*** I(1) 

LAI -3.270*** -8.881*** -3.304** -5.352*** -3.412*** -4.211*** I(0) 

LENT -2.023 -4.220*** -2.152 -3.827*** -1.552 -4.662*** I(1) 

LFA -0.218 -3.562*** -1.652 -3.411*** -0.551 -3.311*** I(1) 

LURBA -0.558 -4.234*** -2.076 -4.166*** -1.652 -4.290*** I(1) 

 

Results of panel cointegration test 

The Pedroni panel cointegration test findings, covering within- and between-dimension evaluations, are provided 

in Table 05. The p-values for the Panel v-statistic and Panel rho-statistic (0.2436 and 0.5612, respectively) are 

greater than the traditional significance threshold, indicating that there is no evidence of cointegration. However, 

the null hypothesis of no cointegration is rejected because the Panel PP and Panel ADF-Statistic p-values (0.000) 

are smaller than the conventional significance criterion. The figures suggest the presence of cointegration. With a 

very high p-value of 0.9271, the Group rho-statistic in the between-dimension analysis suggests there is insufficient 

evidence for cointegration in any of the panels. On the other hand, with p-values of 0.000, the Group ADF and the 

Group PP-Statistic both show evidence of cointegration across the panels. According to Pedroni's cointegration 

methods, each factor is cointegrated over a long period. 

Table 6. Results of Panel Cointegration test 

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

 Statistic Prob. 
Weighted 

Statistics 
Prob. 

Panel v-Statistic 0.34364 0.4123 -1.56832 0.9632 

Panel rho-Statistic 0.56126 0.7127 0.87654 0.8106 

Panel PP-Statistic -3.81602 0.0000 -7.91435 0.0000 

Panel ADF-Statistic -2.07614 0.0000 -4.79875 0.0000 



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

  Statistic Prob.   
Group rho-Statistic 4.71273 0.9271   
Group PP-Statistic -9.53764 0.0000   
Group ADF-Statistic -4.25297 0.0000   

 

Panel ARDL results  

The Panel ARDL model's outcomes, presented in Table 07, demonstrate the intricate dynamics influencing the 

Nordic region's LCF. In terms of LGDP, the short-term coefficient is -0.320 and statistically insignificant, with a 

p-value greater than the typical threshold, while the long-run coefficient is -0.337 and statistically significant at 

conventional levels. This suggests that economic expansion alone may notably contribute to environmental 

degradation in this setting, as GDP has a detrimental impact on the LCF. This outcome agrees with Eleais et al. 

(2024) finding that when GDP rises by 1%, carbon emissions in MENA nations rise by 79.8%. Several researchers 

such as Raihan et al.(2023c) in Malaysia, Ahmad et al.(2024) in China, Addai et al.(2023) in Eastern Europe, Raihan 

et al.(2022b) in China agree with this and concluded that GDP growth degrades the biodiversity. However, due to 

the development of GDP, emissions somewhat decrease in China and India (Raihan et al.,2024c; Raihan et 

al.,2023b). Additionally, an investigation across Vietnam by Minh et al. (2023) shows that economic growth is 

correlated with CO2 emissions up to a specific threshold; after that, CO2 pollutions decline. On the other hand, 

GDP2 has an encouraging link with LCF in both periods. In the short run, the coefficient has a positive value of 

0.681, and in the long run, the value is 0.107. This is significant as the p-value is less than the conventional level 

for both periods. This finding highlights that though shorter period expansion in the economy is not good for the 

Nordic ecosystem, long-term stable GDP is beneficial for biodiversity. However, Ridwan et al. (2024) disagree, 

stating that GDP2 raises CO2 emissions in the South Asian region. 

Similarly, in the short and long terms, there is a beneficial connection between LAI and LLCF; the short-term and 

long-term results are significant as the p values are 0.0388 and 0.0061, respectively. These conclusions demonstrate 

that while AI technology has a favorable advantage on the Nordic ecosystem. LLCF boosts by 0.263% in the near 

run and 0.128% in the long term for every 1% increase in LAI. This could be because the implementation of green 

initiatives may need AI, which can improve energy efficiency across a range of industries, such as manufacturing, 

travel, and residence power. By using artificial intelligence (AI), humans can better manage climate change and 

achieve sustainability while using natural assets (Habila et al.,2023, Raihan et al.2024d). Moreover, technological 

advancements help BRI economies offset the damaging impacts of excessive resource use on the ecology (Majeed 

et al., 2022). Similarly, there is an obvious connection between environmental tax and the environment, as 

evidenced by the beneficial relationship observed between LENT and LLCF across both short and long periods. 

This implies that environmental tax could boost ecological conditions in both terms, and the results are significant, 

with p-values that are less than the usual level in both terms. Specifically, a 1% expansion of LENT in the short 

term will spike LLCF by 0.429% and in the long term by 0.526%. Kartal (2024) found that there is variation in the 

implication of ENT on LCF in G-7 countries. Furthermore, ENT advantages for the United Kingdom, whilst France 

and Germany just partially benefit. Galvez (2024) finds that green taxes in Mexico must be included in the UN 

2030 agenda and that governments should implement and assess them to promote environmental protection in the 

future.  

The table demonstrates a discouraging correlation between LFA and LLCF. The long-term and short-term results 

illustrated statistical significance outcomes as the p-value is less than the conventional thresholds. Specifically, the 

effect indicates that accessibility of finance stimulates higher monetary and business transactions but may not have 



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a positive short-term impact on the ecosystem. Moreover, the financial growth was supported by Shahbaz et al. 

(2017) and Baloch et al. (2019) to enhance the ecosystem condition in technologically advanced Asian nations like 

Malaysia and Indonesia, respectively. According to Hussain et al. (2023), financial accessibility has a long (short) 

term beneficial (negative) influence on carbon emissions in the Asian economies. Similarly, Shang et al.(2024) in 

top emitting countries and Qing et al.(2024) in G-2- economies agree that FA degrades environmental quality. On 

the other hand, Liu et al.(2024) in China and Vietnam and Sharif et al.(2024) in ASEAN territories revealed that 

FA is beneficial for environmental quality. Based on both short and long-term assessments, urbanization (LURBA) 

and LLCF have an adverse interaction. Over time, there will be a -0.567% reduction in LLCF with a p-value of less 

than 0.05 for every 1% expansion of LURBA. A notable short-term decrease in LLCF of -0.876% is associated with 

a 1% rise in LURBA. This could be because of the continual consequences of urban expansion, which lower 

ecological diversity and disrupt the natural world, such as the destruction of trees and the removal of natural 

ecosystems for redevelopment. Voumik and Ridwan (2023) discovered that population expansion in Argentina 

negatively impacts the ecosystem over a long period, supporting this conclusion. Similarly, Malik et al.(2024) in 

Pakistan, Ekeocha (2021) in Africa, and Zhang et al.(2021) in China provided a similar conclusion. On the other 

hand, Khan et al.(2024), in 48 BRI economies, concluded that urbanization reduces emission levels. 

 

Table 7. Results of Panel ARDL test 

Variable Coefficient Std. Error t-Statistic Prob. * 

Long-run Estimation 

LGDP -0.337 2.64166 -4.88469 0.0199 

LGDP2 0.107 0.12212 3.87941 0.0327 

LAI 0.128 0.08704 3.47276 0.0061 

LENT 0.526 0.16386 3.19773 0.0022 

LFA -0.988 0.13977 -3.07333 0.0000 

LURBA -0.567 0.87849 -1.87662 0.0000 

Short-run Estimation 

COINTEQ01 -0.47152 0.1738 -2.712433 0.0087 

D(LLCF(-1)) -0.375935 0.0906 -4.144349 0.0001 

D(LGDP) -0.320 0.97443 -4.09634 0.0774 

D(LGDP2) 0.681 0.65254 3.04459 0.0005 

D(LAI) 0.263 0.22113 4.18998 0.0388 

D(LENT) 0.429 0.43703 -3.98353 0.0294 

D(LFA) -0.112 0.23788 4.47306 0.0379 

D(LURBA) -0.876 1.12977 -4.04147 0.0671 

C 5.499 1.73640 3.16741 0.0024 

 

 

 



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Results of robustness check 

Table 8 describes the utilization of three distinct estimating techniques—FMOLS, DOLS, and FE-OLS to 

investigate the accuracy of the ARDL estimations. The projected LGDP values for each of these methods are -

0.256, -0.085, and -0.219, in that order. These results suggest that the Nordic countries' environmental quality has 

suffered as a result of economic expansion. All estimators have significant coefficients at the 1% level, 

except DOLS, which is significant at the 5%. These outcomes are consistent with the short- and long-term 

conclusions of the ARDL framework. In contrast, LLCF and LGDP2 have an upward correlation, suggesting that 

long-term economic growth has no negative effects on the Nordic ecosystem. In particular, only in the DOLS 

estimation is the coefficient significant at the 10% level; in all other cases, it is significant at the 1% level. Our 

findings of the ARDL estimation are in line with this assessment. In all three examinations, the LLCF variable 

demonstrates a positive correlation with both LENT and LAI. In the FMOLS and DOLS examinations, the LAI 

coefficient values are significant at the 5% threshold; in the FE-OLS test, they are significant at the 1% point. The 

Nordic ecosystems may gain from the adoption and use of AI technology, as evidenced by the fact that for every 

1% growth in AI innovation, LCF expands by 0.076%, 0.210%, and 0.603%, correspondingly. This conclusion 

agrees with the Panel ARDL computations. 

In a similar vein, the LENT and LLCF coefficients exhibit positive correlations in all three estimation techniques. 

To be precise, for each 1% rise in green taxes, LCF climbs by 0.052%, 0.234%, and 0.054%, respectively. In 

calculations using FMOLS, DOLS, and FE-OLS, this variable is statistically significant at the 1%, 10%, and 5% 

levels, accordingly. This outcome illustrates the beneficial effects of environmental levies on the ecosystems of the 

Nordic nations and is in line with the ARDL model. Conversely, the LLCF variable had adverse interactions with 

both LFA and LURBA, suggesting that greater degrees of urbanization and financial accessibility are harmful to 

biodiversity in the areas under consideration. Using all three estimating techniques, the LFA variable is significant 

at the 1% level. In addition, a 1% increase in urbanization (URBA) leads the LLCF for FMOLS, DOLS, and FE-

OLS to drop by 1.562%, 0.543%, and 0.543%, respectively. In the FMOLS and FE-OLS estimations, the variable 

is significant at the 5% level; however, in the DOLS estimation, it is significant at the 1% threshold. The ARDL 

model was the main estimating method employed in this work, and these results validate its conclusions. 

 

Table 8. Results of Robustness Check 

Variables FMOLS DOLS FE-OLS 

LGDP -0.256***(0.0362) -0.085**(0.0012) -0.219***(0.6807) 

LGDP2 0.061***(0.0109) 0.125*(0.0281) 0.011***(0.0782) 

LAI 0.076**(0.0502) 0.210**(0.1520) 0.603***(0.0369) 

LENT 0.052***(0.3025) 0.234*(0.8201) 0.054**(0.0120) 

LFA -0.181***(0.0892) -0.213***(0.0295) -0.177***(0.0675) 

LURBA -1.562**(0.5987) -0.543***(0.0451) -0.543**(0.6043) 

 

 



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D-H Causality test 

Table 9 provides an overview of the findings of the D-H causality assessment for the Nordic economy's LCF. At a 

1% significance level, the p-value of 0.0032 denotes a statistically significant influence of LGDP on LLCF. As a 

result, it is feasible to reject the null hypothesis and conclude that there is a one-way causal connection between 

LGDP and LLCF. In contrast, there is no significant link between LLCF and LGDP because the p-value is larger 

than the predicted threshold, suggesting that development in the economy has a unidirectional consequence on 

natural health in the Nordic region. A closer look indicates that the connections between LAI and LLCF and LGDP2 

and LLCF are also unidirectional. Furthermore, as the null hypothesis cannot be ruled out under these 

circumstances, the findings show that changes in LLCF have no impact on LGDP2 or LAI. On the other hand, 

statistically insignificant higher p-values in each investigation suggest that there is no causal link between LCF and 

LENT. Moreover, there is no correlation across LLCF and LENT or between LENT and LLCF, as the proposed p-

values are higher than the conventional significance thresholds. Nonetheless, there prevails a strong causal 

correlation that is bidirectional within LFA and LLCF as well as between LURBA and LLCF. These coefficients 

are significant at the 1% significance level, with the p-values being 0.0367, 0.0035, 0.0001, and 0.0047, accordingly. 

In conclusion, among all the regressor specifications, LGDP, LAI, LFA, and LURBA are the factors that 

significantly influence changes in the LCF. 

Table 9. Results of D-H Causality test 

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

LGDP  ≠ LLCF 7.03257 3.92056 0.0032 

LLCF   ≠  LGDP 4.2925 1.64693 0.2981 

LGDP2   ≠ LLCF 7.15055 4.01846 0.0521 

LLCF  ≠  LGDP2 4.3399 1.68626 0.1417 

LAI   ≠  LLCF 2.69682 0.32289 0.0468 

LLCF  ≠   LAI 3.11499 0.66987 0.5029 

LENT   ≠  LLCF 3.00297 0.57692 0.5641 

LLCF   ≠  LENT 1.92676 -0.31609 0.1519 

LFA  ≠   LLCF 4.82512 2.08889 0.0367 

LLCF   ≠  LFA 4.87029 2.12637 0.0035 

LURBA   ≠  LLCF 6.97203 3.87033 0.0001 

LLCF   ≠  LURBA 4.70097 1.98587 0.0047 

 

Conclusion and Policy Implications  

Throughout the Nordic countries between 2000 and 2022, this research looked at the complex relationships 

underlying economic growth, artificial intelligence (AI) innovation, environmental taxes, financial accessibility, 

urbanization, and LCF. The study explored the LCF hypothesis using sophisticated econometric techniques to 

identify variables affecting regional load capacity. The analysis used both first-generation and second-generation 

panel unit root examinations, verifying the lack of unit root problems in the dataset to address any methodological 

concerns. Moreover, several panel cointegration experiments revealed long-term cointegration among the 



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parameters under investigation, highlighting their interdependence. Additionally, the chosen variables were 

characterized by both short- and long-term interactions adopting the ARDL framework. 

The FMOLS, DOLS, and FE-OLS techniques were applied extensively to discover the correlations between 

dependent and independent factors and guarantee robust conclusions. The investigation revealed that while financial 

accessibility, urbanization, and short-term GDP development have a detrimental implication on the LCF in the 

selected region, long-term economic growth, AI innovation, and environmental taxation have an advantageous 

effect. Furthermore, the D-H causality examination reveals that no causal association between LLCF and LGD2, 

LAI, and LENT, nor between LLCF and LENT, was found in the study. Moreover, a bidirectional causal association 

was also explored between LFA and LLCF and LURBA and LLCF. The intricate elements influencing load capacity 

in the Nordic area were examined, emphasizing the role of the urban population and sustainable ecosystem. Our 

investigation seeks to establish green urban growth as well as sustainable financial growth and imposition of 

environmental taxation to ensure environmental sustainability in the Nordic territory. Lastly, this study offers a 

basis for enlightened decision-making to boost resilience and prosperity in the area going forward by advocating 

approaches that support conserving the environment and equitable growth. 

The finding of a U-shaped correlation between GDP and environmental sustainability in the Nordic region carries 

significant implications for policy. At first, higher income levels might have a detrimental consequence on 

ecological health since they lead to greater consumption and industrial activity. Nevertheless, once a specific income 

level is surpassed, higher earnings result in more investments in sustainable technology and practices heightened 

environmental consciousness, and more stringent regulatory requirements. To expedite this shift, policymakers 

should provide incentives for green investments and sustainable inventions, especially during the initial phases of 

economic expansion. Introducing progressive environmental levies and subsidies for clean technologies can help 

alleviate the adverse effects of increasing wealth. In addition, promoting collaborations between the public and 

commercial sectors and allocating resources towards environmental education can bolster public backing for 

sustainability endeavors. By employing strategic management techniques, it is possible to ensure that rising earnings 

contribute to enhanced environmental sustainability. This involves effectively balancing economic expansion with 

the preservation of ecological resources. 

This study found that the application of AI technology and the implementation of a green tax contribute to the 

advancement of environmental sustainability in the Nordic region. Policymakers should give utmost importance to 

the incorporation of AI technology in order to improve environmental management, increase efficiency, and 

promote sustainability in many sectors. In order to accomplish this, governments can offer incentives, subsidies, 

and grants to both startups and existing enterprises that are engaged in developing AI advancements that have 

positive consequences on the environment. Simultaneously, the implementation and modification of environmental 

levies to accurately represent the actual cost of pollution can efficiently deter environmentally detrimental practices 

and produce income for sustainability initiatives. The taxation system should be structured in a progressive manner, 

with the aim of specifically targeting large-scale polluters while minimizing the financial strain on low-income 

populations. Proceeds derived from environmental levies might be allocated towards the development of eco-

friendly infrastructure, advancement of clean technology through research and development, and promotion of 

public awareness initiatives. In addition, authorities should promote collaboration between the public and private 

sectors in order to establish a conducive environment for the development of AI advances. Nordic nations can 

strengthen their environmental sustainability, create economic growth, and continue their leadership in global 

sustainability efforts by strategically using AI innovation and implementing environmental levies. 

The findings also indicated that the growth of financial accessibility and urbanization have a negative impact on 

environmental sustainability in the Nordic region. Policymakers should prudently oversee financial accessibility to 

prevent excessive borrowing and uncontrolled economic growth. This can be accomplished by allocating financial 



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resources to green investments and sustainable projects rather than industries with significant emissions. Enforcing 

rigorous environmental norms and standards for urban development is essential to minimize the negative impacts 

of urbanization. Encouraging sustainable urban planning methods, such as implementing green construction rules, 

establishing efficient public transit networks, and preserving green spaces, can effectively mitigate the 

environmental consequences of expanding urban areas. Moreover, providing incentives to encourage the use of 

environmentally friendly technologies in urban infrastructure and housing can effectively improve sustainability. 

Nordic nations may achieve long-term sustainability by including sustainability standards in their financial and 

urban development policies. This approach allows them to strike a balance between economic growth and 

environmental preservation, even while financial accessibility and urbanization continue to rise. 

 

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: Available on request 

 

Authors contribution: All authors contributed significantly to the work. Md Sibbir Hossain handled data analysis, 

while Mohammad Ridwan led the conceptualization, supervision, and manuscript drafting. Afsana Akhter assisted 

with the literature review and data validation. Md Boktiar Nayeem and Md Tazwar Hossain Choudhury were 

involved in data collection. Md Asrafuzzaman, Shaharina Shoha, Shake Ibna Abir, and Sumaira contributed to 

manuscript editing and revisions. 

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