Analyzing How AI impact Environmental Sustainability: Case Study for USA

Analyzing How AI impact Environmental Sustainability: Case Study for USA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analyzing How AI impact Environmental Sustainability: Case Study for USA Ayodele Oluwaseun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5953542/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the role of private investment in Artificial Intelligence (AI) in promoting environmental sustainability in the United States from 1990 to 2019. It also analyzes the impact of financial globalization, technological innovation, and urbanization by testing the Load Capacity Curve (LCC) hypothesis. The study employs stationarity tests, which indicate that the variables are free from unit root problems and exhibit mixed orders of integration. Using the Autoregressive Distributive Lag (ARDL) Model bound test, the study finds that the variables are cointegrated in the long run. The short-run and long-run estimations of the ARDL model confirm the existence of the LCC hypothesis in the United States, revealing a U-shaped relationship between income and load capacity factor. The results show that private investment in AI has a significant positive correlation with the load capacity factor, thus promoting environmental sustainability. Conversely, technological innovation and financial globalization exhibit a negative correlation with the load capacity factor in both the short and long run. To validate the ARDL estimation approach, the study employs Fully Modified OLS, Dynamic OLS, and Canonical Correlation Regression estimation methods, all of which support the ARDL results. Additionally, the Granger Causality test reveals a unidirectional causal relationship from private investment in AI, financial globalization, economic growth, technological innovation, and urbanization to the load capacity factor. Financial Globalization LCC Hypothesis Private Investment in AI Technological Innovation United States 1. Introduction The destruction of the natural environment is one of the most pressing issues facing the modern world today (Raihan et al.,2024; Li et al.,2021; Liu et al., 2021). This is due to its adverse effects on the overall economy, biodiversity, the atmosphere, human health, the quality of the air, and assets including groundwater, soil, and forests (Rehman et al., 2021). Globally, maintaining economic growth and reducing climate change now depend on reducing CO2 emission level and maintaining ecological integrity (Raihan et al., 2022). Moreover, a great deal of this emission originates from a small number of nations (Magazzino et al., 2020). Even though the US economy has been expanding for over three decades, the nation is dealing with major environmental problems (Koondhar et al., 2018). As of right now, China is the nation with the fastest pace of economic growth, with the United States standing in second (He and Richard 2010). Even though China accounts for 28% of global CO2 emissions, the USA is responsible for 16%, the EU for 11%, India for 6%, and other countries for 39%. However, as China has a population four times that of the USA, the USA has higher CO2 emissions per capita than China (Koondhar et al., 2018). Moreover, according to 2020 year-end data (WorldBank, 2021) the USA produces almost 14%.These alarming figures underscore the relevance of the present research from a US perspective. Significant outcomes can be obtained by utilizing relevant factors including financial globalization, technical innovation, and private investment in AI. The research findings can be implemented by policymakers to ensure environmental sustainability in the United States. The effect of multiple socio-economic and technical variables on carbon emissions has been a focus of numerous researches (Orhan et al., 2021; Su et al., 2021; Zhang et al., 2021; Guloglu et al.,2023 and Raihan et al.,2023). Even if carbon emissions make up a sizable amount of greenhouse gases, Akinsola et al. (2021) claimed that carbon emissions are insufficient to accurately depict and assess total environmental damage. On the other hand, the ecological footprint was first proposed by Rees (1992), and it was subsequently emphasized by Galli et al. (2012) as one of the most extensive economic-ecological indicators for evaluating environmental damage. In spite of this, not much research has been done on the Load Capacity Curve (LCC) concept. Therefore, there is a lack of knowledge in the literature about the LCC hypothesis's validity in emerging economies like the USA. To close this gap, this research assesses the LCC hypothesis's relevancy to the USA. According to Siche et al. (2010), the load capacity factor offers a more precise ecological assessment. The load capacity factor shows how strong or able a country is to support its citizens according to their modern lifestyles (Xu et al., 2022). Therefore, an ecosystem is considered to be unsustainable when the LCF is less than 1, and sustainable when the LCF is greater than 1 (Pata et al., 2021). We have recently witnessed revolutionary developments in several industries as Artificial Intelligence (AI) has become increasingly integrated and the environment field is no exception. The issue of global sustainable development can be resolved somewhat by the progress of AI technologies. Moreover, the implementation of AI can lower emissions to the environment (Shang et al.,2024). AI boosts Chinese industries' environmental sustainability and dramatically lowers the intensity of pollutant emissions (Cheng et al., 2024). Artificial intelligence-driven commercialization is expected to reach $ 3.9 trillion in 2022, up from $ 1.2 trillion in 2018, which marked a 70% growth from 2017 (Brown, 2013; Fatorachian and Kazemi, 2018; Richards et al., 2019). The public sector's contribution to AI has been expanding over the past few decades, as seen by the $ 3.2 billion in investments made by the U.S government in 2022 (JEC,2023). Additionally, in almost 65% of AI-enabled environmental initiatives, mathematical models are used. All environmental professionals are likely to gain numerous advantages from AI tools (Konya and Nematzadeh, 2024). It can assist policymakers in formulating scientifically grounded strategies and plans for the sustainability of the environment (Asadnia et al.,2014; Asadnia et al.,2017; Farahnakian et al., 2011). AI-driven technologies are crucial for ecological resource conservation as they facilitate the monitoring and preservation of natural habitats, animal populations, and ecosystems (Krishnamoorthy and Sistla, 2023). To discover biodiversity hotspots, monitor endangered species, and identify threats to the environment like deforestation, poaching, and pollution, machine learning algorithms can examine satellite images, sensor data, and ecological parameters (Krishnamoorthy and Sistla, 2023). Stakeholders can safeguard biodiversity and ecosystem services by implementing focused interventions and making educated decisions by utilizing AI for environmental monitoring and conservation (Sistla and Konidena, 2023). There are several ways in which the process of economic growth might result in environmental damage (Kartal et al., 2022). Growth in the economy comes with a substantial consumption of energy, natural assets, and production inputs, which initially pollutes the environment and puts more strain on it (Nurgazina et al., 2022). With time, the pressure on the ecosystem declines, and as environmental knowledge and demand grow, higher income levels help to improve environmental quality (Pata et al.,2023). On the other hand, in Bangladesh and Indonesia, Bakirtas et al. (2023) discovered a reverse U-shaped link between GDP and LCF. In 2024, the United States accounted for 13.3% of global GDP and for around 4.21% of the global populace (World Bank, 2024). Meanwhile, almost 16% of global CO2 emissions came from 5,416 MT of emissions in the United States (BP, 2020). Since the US is one of the major emitters of greenhouse gases into the atmosphere, it bears some of the blame for the climate crisis and global warming. The catastrophic consequences of the United States' roughly 1.0 degree Celsius climate change are already worrisome, affecting the most vulnerable members of the population with a climate-fueled disaster that caused fatalities, deteriorating health, a low standard of living, and even the destruction of the earth's ecosystem (Zhang et al.,2023). The majority of research has shown that the growing population causes more environmental damage (Voumik and Ridwan, 2023 ; Khan et al., 2021; Pham et al., 2020; Menz and Welsch, 2012). Financial globalization (FGOB) considers characteristics such as international assets and liabilities, FDI, investment portfolios, and related laws to assess how far a nation has incorporated into the global financial system. As a result, FGOB is a noteworthy measure of financial progress (Dhingra, 2023; Wang et al., 2023). With the progress of financial globalization, foreign direct investment is increasing globally. The most current UNCTAD (2020) showed that from $ 1.3 trillion in 2018 to $ 1.5 trillion in 2019, there was a 3.0% rise in worldwide FDI inflows. Scholars like Furceri et al. (2019), Usman et al. (2019), Raghutla and Chittedi (2020), Obstfeld (2021), Gungor et al. (2021), and Awosusi et al. (2022) characterize financial globalization as the convergence of global monetary systems into a single market. Through the optimization of resources, clean energy, garbage disposal, prevention of pollution, and monitoring of the atmosphere, technological innovation can slow down the decline of the environment (Ha 2022; Vyas et al. 2022; Ramzan et al. 2023). Consequently, our investigation contributes significantly to the collection of current literature in several ways. First of all, from a U.S. viewpoint, it addresses the largely unexplored field of private investment in AI, which makes it distinctive. The experimental research has presented consistent results concerning the correlation between load capacity factor (LCF) and private AI investment (PAI). This study aims to clarify the linkages between LCF and PAI in light of the situation described above, offering additional relevant data for designing green policies. Second, the study makes use of unique PAI data that is categorized as Estimated Investment in AI (US $ ) and is derived from Our World in Data. Within the framework of the USA's load capacity factor (LCF), this analysis focuses on the trends and key research areas of private investment in artificial intelligence (AI), financial globalization, technical innovation, economic development, and urbanization. Analyzing the LCF within the context of the USA will offer fresh perspectives to scholars exploring the issue and establish a noteworthy contribution to the body of understanding. As far as we are aware, our investigation is the first to conduct a detailed review of the literature on the LCF, enabling us to embark on the following research goals: What effect do PAI and FGOB have on the USA's LCF? In what ways can independent and dependent variables interact spontaneously? Furthermore, how do TI, GDP, and URBA affect the LCF? The significance of this research lays in the fact that private investment in artificial intelligence and financial globalization has not been extensively studied in other studies. By recognizing these elements, policymakers and strategy developers might be able to more effectively promote environmentally responsible behavior. More research in this area is essential to building a pleasant and sustainable environment, particularly in light of increasing interest in green cities and public awareness of ecological issues. The effects of GDP, PAI, FGOB, TI, and URBA on the LCF are examined in inquiry using ARDL methodologies from 1990 to 2018. Additionally, the robustness of the outcomes was checked as well using the FMOLS, DOLS, and CCR techniques. This study delivers valuable insights for legislators in the USA and other nations to achieve the SDGs while simultaneously promoting sustainable economic growth and increasing the quality of the environment (as evaluated by the LCF) by adopting an integrative approach to the issue. The paper examines the body of research on the chosen determinants in the second part. The information, theoretical framework, empirical model development, and estimating methods used to conduct the study are all covered in detail in the "Methodology" section. "Results and Discussion," the fourth part, offers an extensive discussion of the model's findings. The fifth and last part summarizes the analysis and suggested strategies of action. 2. Literature Review The impact of financial globalization, advancements in technology, and economic expansion on the load capacity factor (LCF) has been the subject of several empirical studies. While many analyses examined the ARDL model, the majority of papers focused on how trade openness, urbanization, and green energy usage affect environmental quality. Others have focused on analyzing the connection between trade openness, globalization, economic growth, and LCF. Previous studies on the concept of ecological degradation in the context of the USA have not yet been extensively conducted as this is a relatively new field. However, the investigation used some prior studies that assisted with the selection of variables and methods. This section will cover a few of these inquiries. 2.1 Economic Growth and Load Capacity Factor The relationship between economic development and environmental sustainability has been the subject of several studies. Many believe that as the economy grows there will be an increase in emissions of CO2. However, things become more complicated when we include load capacity in addition to CO2 emissions as environmental quality criteria. People's goals for monetary progress encourage them to use all available energy resources, which has an economic effect of producing emissions (Panel et al., 2011). In addition, when business activity expands to achieve incredible growth, natural resource depletion takes place (Teng et al.,2024). Thus, the reduction of biocapacity, biodiversity, and the load capacity factor in different economies and areas can be linked to greenhouse gas emissions and resource depletion for income development (Zhang et al., 2022). In Pakistan, Ali et al. (2023) performed an experiment using a "dynamic autoregressive distributed lags model" and a unique approach called "Kernel-based regularized least squares (KRLS)." They discovered an unexpected negative correlation (-0.270) between the load capacity factor and GDP growth over the long run. According to Pata (2021), growth in GDP significantly degrades the environment in a manner that cannot be balanced by renewable energy sources or increased healthcare costs in the United States. Similar research (Fareed et al. 2021; Huilan et al.,2022; Shang et al. 2022; Abdulmagid Basheer Agila et al. 2022; Jin and Huang.,2023; ÇAMKAYA and KARAASLAN.,2024) that used the LCF as the dependent variable discovered that increases in economic growth exhibited a negative consequences on the LCF indicating the destruction of the ecosystem. By examining the consequences of GDP, Li et al. (2023) seek to understand how the next eleven countries improved their LCF between 1990 and 2018. The long-term findings indicate that reliance on economic growth reduced LCF. However, Ullah et al. (2023) insist that while there is no short-term effect, a growth in economic complexity has a positive long-term influence on the LCF. On the other hand, a U-shaped link between income and the quality of the environment was observed by Guloglu et al. (2023), confirming the validity of the LCC theory. 2.2 Private Investment in AI and Load Capacity Factor The increasing prevalence of artificial intelligence (AI) technology in our daily lives has extensive political and socioeconomic implications. Both officially and privately funded AI research and applications are encouraged heavily (Brandusescu, 2022). Negi (2018) focuses on the flow of investment in artificial intelligence from the top three major nations in the field China, India, and the United States. The research illustrates the steps that the government has taken to incorporate artificial intelligence into its present ecosystem, which is supported by the private sector. The t-test demonstrates a significant relationship between annual investment and trend analysis, which suggests that the AI industry is growing quickly. Vietnam is seeing a relatively small amount of misdirected AI investment. Vietnam excessively remains significantly behind other Southeast Asian nations, necessitating both governmental and private investment in this sector (Pham et al.,2024). Artificial intelligence (AI) investment from the private sector has an enormous impact on the environment, positive as well as negative. AI helps with the preservation of natural assets, controlling energy consumption, ecological safeguarding, pollution control, agriculture, and other areas, all of which are critical to attaining environmental sustainability (Kumari and Pandey,2023). In a similar vein, Habila et al. (2023) indicate that the use of AI improves human capacity to manage climate change to achieve sustainability while utilizing natural resources. On the other hand, Okengwu et al. (2023) reveal that increased usage of AI in agriculture results in increased carbon emissions that affect humanity and the natural world. Green AI can boost productivity and alleviate its negative effects on the environment (Pachot and Patissier, 2022). Since private investment in AI usually has a negative impact on the natural world, government officials need to advocate for increased private investment, particularly in green AI. 2.3 Financial Globalization and Load Capacity Factor Financial globalization is the uncontrolled and free movement of financial resources across national boundaries (Kose et al. 2009).Both positive and negative effects of financial globalization on the load capacity factor are apparent in emerging countries. In the instance of India, Akadiri et al. (2022) demonstrated that FGOB is positively connected with the LCF both in the short and long run. According to Raihan et al. (2021), the short- and long-term consequences of FGOB are favorable for the load capacity factor. By taking into account financial globalization over the years 1980–2021, Ozcan et al. (2024) aim to look at how Germany’s environmental quality is affected. Through the use of advanced quantile-based methods, they highlight how financial globalization boosts the quality of the environment. Moreover, using panel econometric approaches, Wang et al. (2022) scrutinized the most recent yearly data set that included 31 OBOR countries from 1996 to 2018. The environment deteriorates due to financial globalization, according to the findings. Many scholars also agreed that FGOB slows down environmental damage by increasing the LCF (Jin et al.,2023; Xu et al.,2022;Pata et al.,2021;Yang et al.,2023). However, in the case of Bangladesh, the effects of financial globalization on the ecosystem are multifaceted and rely on several variables, including clean FDI and the use of renewable energy (Murshed et al. 2021). From 1990 to 2017, Kihombo et al. (2022) checked out the link between environmental impact and financial globalization in some West Asian and Middle Eastern (WAME) countries. The long-run predictions from continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) tests demonstrate that, through lowering the ecological footprint, financial globalization contributes a major part in promoting environmental sustainability. Several outcome was also observed by (Awosusi et al.,2022: Ulucak et al.,2020; Tahir et al.,2021) and they reveal that a positive effect of FGOB on the LCF, indicating that the enhancement of environmental quality. In light of these outcomes, it is essential to determine if financial globalization provides the USA with a comparable opportunity to boost its load capacity factor. 2.4 Technological Innovation and Load Capacity Factor Previous research has mostly overlooked technological innovation (TI), and it has been found to have both positive and negative consequences. The continuous improvement in the degree of innovation has rendered policymakers as well as scholars recognize the significance of technological innovation in preventing environmental deterioration (Du et al., 2022; Haldar and Sethi, 2022). Numerous studies have been performed to analyze the fundamental connections between LCF and technological progress. The MMQR approach is used by Jahanger et al. (2024) to analyze the consequences of technological innovation on LCF between 1994 and 2018. The results of the study demonstrate that, in the top 10 SDG nations, TI has an adverse and substantial effect on lowering LCF. To examine how technological innovation affects environmental quality in China, Kartal and Pata (2023) consider environmental indicators such as carbon dioxide (CO2) emissions, ecological footprint (ECF), and load capacity factor (LCF). The findings exhibit that whereas TIN reduces LCF at middle quantiles, it increases CO2 emissions and ECF at higher quantiles. Several analysis also found that technological innovation is hazardous for the ecosystem (Raihan et al.,2024;Su et al.,2023; Adebayo et al.,2022). On the other hand, Wang et al. (2020) researched N-11 economies between 1990 and 2017, utilizing the unit root test, augmented mean group, and common correlated effect mean group proposed by Pesaran (2007) serve as the foundation for the empirical estimations. The outcomes illustrate that technological innovation has a negative relationship with carbon emissions, which boosts the quality of the environment. Furthermore, Kihombo et al.(2021) assessed the impact of TI on environmental quality in West Asian and Middle Eastern countries and demonstrated that TI strengthens the natural world. Similarly to this, Rafique et al. (2020) provided evidence that technical advancement lowers pollution in the BRICS countries. Multiple studies as well as showcase the positive consequences of technological innovation on maintaining economic sustainability (Mehmood et al.,2023; Khan et al.,2023; Anwar et al., 2021). 2.5 Urbanization and Load Capacity Factor The goal for individuals shifting from rural to urban areas is to lead ordinary lives while working in industries that generate revenue (Ruel et al., 2008). Furthermore, the concept of the smart city, which advocates for energy from both renewable and nuclear sources, has emerged as the primary goal of industrialized and modern society (Chenic et al., 2022). The ARDL approach was utilized by Raihan et al. ( 2023 ) to investigate cointegration and both short- and long-term dynamics using time series data from 1971 to 2018. The result illustrates that urbanization lowers Mexico’s load capacity factor and, therefore lowers the quality of the environment. Urbanization has a detrimental impact on the dynamics of load capacity, which accelerates environmental deterioration (Teng et al.,2024). Using Cross-Sectional ARDL estimators and AMG estimators, Shah et al. (2023) performed a study in the top 15 nations that produce natural gas and discovered that urbanization has a significant impact on environmental damage. Similarly Raihan et al.(2024) and Caglar et al.(2023) also concluded that urbanization is harmful for the ecosystem. However, the relationship involving urbanization and ecological sustainability is investigated by Fang et al. (2024) using the frequency domain causality technique and the ARDL estimator. The load capacity factor curve theory is supported in Thailand as the ARDL estimator finding shows that urbanization reduces LCF. Zhu et al. (2018) came to the same conclusion that URB makes the natural world better. Additionally, Xu et al. (2022) assessed how urbanization affected the load capacity factor in Brazil between 1970 and 2017. Surprisingly, the outcome of the ARDL approach revealed that the LCF is not affected by urbanization in Brazil. Similar findings have been reported by Chen et al. (2022) using CCEMG and AMG tests for the years 1990–2019 and Haseeb et al. (2018) using FMOLS from 1995 to 2014, indicating that URB had no appreciable effect on the environment quality for the BRICS countries. 2.6 Literature Gap The relationships between load capacity factor, financial globalization, the private investment in AI, technological development, economic expansion, and urbanization in the USA have not, as far as we are aware, been investigated. Although researchers have looked into these areas on their own, they haven't consistently merged their discoveries. Previous research efforts demonstrated several shortcomings, especially an absence of comprehensive analyses of the connection between PAI and LCF in the USA. Private sector AI investments could aid agriculture, promote renewable energy, mitigate risks like oil spills, and develop sustainable practices, thereby reducing global warming risks. All of these aspects constitute PAI an entirely new field to investigate for the USA perspective. To cover up these deficiencies, this study explores the link between PAI and the environment utilizing strong statistical approaches such as ARDL, FMOLS, DOLS, and CCR procedures. Through a review of these procedures, the USA might find out if harnessing technical innovation, monetary integration, and business growth can offer the possibility of elevating its load capacity factor and bringing it into line with broader global shifts toward improved environmental sustainability. The implications of the load capacity factor in this area have not yet been the focus of inquiry. As a result, this investigation considers these components as essential to long-term environmental sustainability. By assisting stakeholders and lawmakers in establishing policies that are customized to the distinctive ecological and socioeconomic dynamics of the United States, this analysis advances sustainable development. 3. Methodology 3.1 Data and Variables The ongoing research analyzed data to check out how technical advancements, financial globalization, GDP, urbanization, and private investment in AI influenced the USA's LCF between 1990 and 2019. The United States gathered attention because of its sustainability concerns and data accessibility. The World Development Index (WDI) is the source of the GDP and URBA figures. Here, we consider the LCF as an endogenous variable that derives from GFN and is utilized as a substitute for ecological sustainability. Our World in Data is the same source from which PAI and TI information was collected. Conversely, the FGOB info is adopted from the KOF Globalization Index. In addition, we selected FGOB, TI, and PAI as our investigation's policy variables. Table 01 Source and Description of Variables Variables Description Logarithmic Form Unit of Measurement Source LCF Load Capacity Factor LLCF Gha per person GFN GDP Gross Domestic Product LGDP GDP per capita (current US $ ) WDI PAI Private Investment in AI LPAI Estimated Investment in AI (US $ ) Our World in Data FGOB Financial Globalization LFGOB Globalization Index KOF Globalization Index TI Technological Innovation LTI Patent applications, residents Our World in Data URBA Urbanization LURBA Urban population (% of total population) WDI 3.2 Theoretical Framework The most important instrument in the realm of the environmental field is the load capacity curve (LCC), which offers fascinating details on the complex links between ecological sustainability, economic success, and progress in humanity. This is significant as it illustrates the balance—or absence between the planet's capability of restoring its natural resources (biological capability) and the utilization of human capital (ecological footprint). Since biocapacity and EF are incorporated in the denominator of the LCF, a greater LCF is symbolic of a healthier environment (Pata and Kartal, 2023). The LCF offers a more comprehensive ecological assessment by contrasting biocapacity and ecological footprint (Dogan and Pata, 2022). Furthermore, the LCC highlights the interconnectedness of the world's biological issues as claimed by Wu et al. (2023), including climate change, resource scarcity, and loss of biodiversity. It is believed that the LCC has a U-shaped connection, with GDP constituting as the main driver. According to Pata and Tanriover (2023) and Pata and Ertugrul (2023), there are distinct trends in the implications of GDP on the environment, suggesting a U-shaped curve connection. The awareness that resource usage grows in tandem with economic expansion and developments in personal wealth is highlighted by this relationship as a crucial aspect of ecological sustainability (Degirmenci & Aydin, 2024). Financial globalization promotes cross-border economic activity, which elevates national manufacturing and, consequently, exacerbates environmental degradation (Xu et al.,2022). Additionally, the rise of cities and industries is fostered by urbanization and financial globalization, which increases the demand for energy to support the expansion of infrastructure and industrial processes, both of which harm the atmosphere (Ahmed et al., 2021; Tufail et al.,2022; Yang and Khan,2022). The results by Caglar et al.(2023) validate the load capacity curve (LCC) concept by demonstrating that monetary eventually becomes an ecologically beneficial factor. As was previously indicated, there may be a range of linkages between the components, including GDP growth, technical innovation, and private investment in AI, urbanization, financial globalization, and load capacity factor. To improve knowledge of previous research, we have created the following Eq. (1) for LCC theory: $$\:Load\:Capacity\:Factor=f\left(GDP,\:{Y}_{t}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ Here, Y t is a variable for additional parameters impacting the load capacity factor, while GDP is a variable for income in Eq. (1). Eq. (2) seeks to provide a deeper comprehension of the factors impacting the load capacity factor by including additional relevant variables such as urbanization, financial globalization, private investment in AI, and economic growth. $$\:LCF=f\left(GDP,\:PAI,FGOB,\:TI,\:URBA\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)\:$$ The load capacity factor in Eq. (2) is represented by LCF, whereas the terms financial globalization (FGOB), technological innovation (TI), urbanization (URBA), and private investment in artificial intelligence (PAI) are introduced to symbolize particular principles. The econometric explanation of Eq. (3) is given above. $$\:{LCF}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}GD{P}_{it}++{\alpha\:}_{2}{PAI}_{it}+{\alpha\:}_{3}{FGOB}_{it}+{\alpha\:}_{4}{TI}_{it}+{\alpha\:}_{5}{URBA}_{it}\:\:\:\left(3\right)$$ Equation (4) illustrates the variables' logarithmic values. It increases understanding and facilitates the formulation of conclusions based on statistics by breaking down complicated connections into more straightforward linear forms. Logarithmic scales can manage data of various sizes and assist with heteroscedasticity when broad ranges need to be minimized. $$\:{LLCF}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}LGD{P}_{it}+{\alpha\:}_{2}{LPAI}_{it}+{\alpha\:}_{3}L{FGOB}_{it}+{\alpha\:}_{4}L{TI}_{it}+{\alpha\:}_{5L}{URBA}_{it}\:\left(4\right)$$ Here, the research's coefficients are displayed in the parameter range of \(\:{\alpha\:}_{0}\) to \(\:{\alpha\:}_{6}\) in Eq. (4). 3.3 Econometric Framework This investigation deployed the ARDL technique for data estimation to explore the link between LCF and variables like GDP growth, PAI, FGOB, TI, and URBA in the USA. We additionally utilized the FMOLS, DOLS, and CCR approaches to guarantee robustness. To ensure stationarity, the unit root tests (ADF, P-P, and DF-GLS) were performed at the beginning of the study. Because of the nature of the time series data, the ARDL bound test was then implemented. The ARDL (both short run and long run) estimate was then carried out. Ultimately, after an elaborate estimating procedure, we determined which econometric model was the most efficient and trustworthy. 3.3.1 Unit Root test To ensure consistency in information, a regression test was conducted to eliminate unit roots across all variables. This is important because factors involving unit roots or non-stationary data must assist in explaining a greater proportion of the results to prevent the drawing of incorrect conclusions (Nelson and Plosser, 1982; Engle and Granger, 1987). It is essential to use a unit root test to prevent incorrect regression. The stationary nature of the regression variables is confirmed by differences and stationary processes (Raihan and Tuspekova,2022; Raihan et al., 2022). The empirical research's findings indicate that before applying cointegration approaches, the integration sequence must be determined (Sahoo and Sethi, 2022). A time series' stationarity or non-stationarity is calculated by Voumik and Ridwan ( 2023 ), since it is critical in identifying non-stationary data that might produce inaccurate results. To observe the stationarity within the data set, this research adopted the Dickey Fuller-Generalized Least Squares (Elliot et al., 1992) unit root test, the Philips Perron (Philips and Perron, 1968), and the Augmented Dickey-Fuller (Dickey and Fuller, 1979) unit root examination. Due to its ability to control serial autocorrelation, the ADF technique has become more popular (Dickey and Fuller, 1981). Compared to the Dickey-Fuller (DF) approach, the ADF technique is more robust and applicable to more sophisticated procedures (Fuller, 2009). The objective of employing the unit root test was to approve that no variable surpassed the integration order, thus bolstering the methodological soundness of the ARDL simulation (Raihan,2024). 3.3.2 Autoregressive Distributive Lag Model The ARDL test was developed by Pesaran et al. (2001) and widely utilized due to its robustness and adaptability in handling different degrees of variable integration. If the indicators are integrated at the I(0) or I(1) level, the ARDL Bounds testing method can be applied, in contrast to traditional cointegration assessments. This approach is beneficial even with a small sample size since it produces dependable and consistent estimates even when there are only a limited number of data point’s available (Ridzuan et al.2023;Pattak et al.,2023). The longer-period relationship between LCF, GDP, PAI, FGOB, TI, and URBA is shown by Formula (8). This method was created to assist in defining ARDL Bounds: We compare the alternatives, which state that there is evidence of cointegration, with the null hypothesis, which states that there is no cointegration. It is possible to conclude that the variables are long-term correlated if the F-statistics are larger than the highest critical value for rejecting the null hypothesis. If the F-statistic is smaller than the lowest allowable value, the null hypothesis is accepted. If the F-statistics are seen to be between the lowest and maximum limits, the test is considered inconclusive (Raihan et al.,2023). Equations 5 and 6 reveal the null and alternative hypotheses: $$\:{H}_{0}={\sigma\:}_{1}={\sigma\:}_{2}={\sigma\:}_{3}={\sigma\:}_{4}={\sigma\:}_{5}={\sigma\:}_{6}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(5\right)$$ $$\:{H}_{1}={\sigma\:}_{1}\ne\:{\sigma\:}_{2}\ne\:{\sigma\:}_{3}\ne\:{\sigma\:}_{4}\ne\:{\sigma\:}_{5}\ne\:{\sigma\:}_{6}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(6\right)$$ The signs, "H0 and H1" denoted the null hypothesis and the alternative hypothesis respectively. This study assessed the error correction model (ECM) after determining the long-term connections to investigate the short-term behavior of the independent variables and the short-term adjustment rate toward the long-term rate (Luqman et al., 2021). To do this, the ECM is included in the ARDL structure, as shown in Eq. (6) Here, the notion ℓ is the rate of adjustment. 3.3.3 Robustness Check This study scrutinized the FMOLS, DOLS, and CCR technique to represent the long-run impact of GDP, PAI, FGOB, TI, and URBA on LCF in order of evaluating the stability within the ARDL long-term estimation. When there is evidence of series cointegration, FMOLS and DOLS can be utilized. However, the biggest advantage of the DOLS estimation is its ability to present different levels of integration of discrete elements inside the cointegrated framework (Pesaran, 1997; Raihan and Tuspekova, 2022). The FMOLS technique was developed by Hansen and Phillips(1990). When addressing cointegration and its influence on autocorrelation and endogeneity in the explanatory variables, the FMOLS technique modifies the least squares approach (Pattak et al.,2023). By estimating the dependent variable on explanatory factors in levels, leads, and lags, the DOLS technique successfully permits individual variables in the cointegrated outline to be integrated when a mixed order of integration occurs (Raihan et al.,2023).The CCR technique was developed by Park (1992) and merely utilizes the static part of a lagged model to convert data. In a cointegrating system, the CCR ensures that data extracted from explanatory variables on unobserved heterogeneity will show at zero frequency. As a result, the CCR approach produces chi-square and arithmetically effective approximation assessments devoid of any undesirable aspects. We therefore use the FMOLS and DOLS estimators to determine elasticity over the long run. What follows is Equivalent to the FMOLS equation is Exhibit 8. Here, the longer-period flexibility is assessed using the FMOLS and CCR coefficients and t shows the time-varying trend. 3.3.4 Pairwise Granger Causality test The concept of a causality analysis aims to determine whether or not previous changes in a factor are responsible for the current observation, as theoretical correlations may not hold in practice due to certain elements that may not be clearly described in theory (Voumik et al.,2023). This work employs the Pairwise Granger causality test which is a statistical concept of causation based on a prediction that offers several advantages over other time-series research methodologies (Winterhalder et al. 2005). To say that X is causally related to Y would be to say that the sum of X's prior and current values deviates substantially beyond 0. Y and X causality are subject to the same laws; if the results deviate from zero, it indicates the presence of causation on both sides. The analysis used the paired Granger causality test introduced by Granger (1969), to ascertain if there prevailed a short-term causal link between the components. Eq. (9) shows that Xt and Yt are causally related. $$\:E\left({Y}_{t+h}|{J}_{t,}{X}_{t}\right)=E\left({Y}_{t+h}|{J}_{t}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(9\right)$$ Here, Jt notation is used for the sets of information gathered from all of the outcomes up to a certain point of time (t). 3.3.5 Diagnostic test Several diagnostic tests were implemented in this research to confirm the data's heteroscedasticity, serial correlation, and normality. The Lagrange Multiplier (LM) test, the Jarque-Bera test (Jarque and Bera, 1987), and the Breusch-Pagan-Godfrey test (Breusch and Pagan, 1979) serve as essentials for validating model assumptions and guaranteeing the robustness of results in time series analysis. Since many econometric models assume normally distributed errors for successful inference, the Jarque-Bera examination checks the normality of residuals, a vital phase in the process. By detecting serial correlation in residuals, the Lagrange Multiplier test makes sure that errors do not correlate over time, which might result in misleading and biased estimations. The heteroscedasticity, or non-constant variance of residuals, is verified using the Breusch-Pagan-Godfrey test, which can lead to inaccurate standard errors and estimates. 4. Result and Discussion 4.1 Summary Statistics The summary statistics for the variables we investigated with 32 observations are displayed in Table 02 below. The descriptive data for the USA for the following seven variables are provided (LLCF, LGDP, LGDPSQ, LPAI, LFGOB, LTI, and LURBA). As can be seen in the table, all of the variables we chose had a positive mean except LLCF, and the mean was the highest for LGDPSQ. Furthermore, the estimated standard deviations of each variable are quite small, implying that the data points are centered around the mean and have minimal periodic variability. Just LLCF and LPAI showcase positive skewness among the variables; in contrast, LGDP, LGDPSQ, LFGOB, LTI, and LURBA exhibit negative skewness. Furthermore, the Jarque-Bera normality test was applied to ensure that each variable in this investigation had a normal distribution. Since this test accounts for both skewness and any anomalous Kurtosis, it seems logical. Key indicators of statistics including mean, median, maximum, minimum, standard deviation, probability value, total, and sum square are illustrated in Table 2 , which offers an exhaustive analysis of the information at hand. Table 02 Descriptive statistics of Variables Statistic LCF LGDP LGDP2 LPAI LFGOB LTI LURBA Mean -0.835416 10.64393 113.3917 22.0143 4.290778 12.1409 4.377885 Median -0.822656 10.71885 114.8942 21.2377 4.322086 12.27706 4.382195 Maximum -0.63269 11.15938 124.5318 25.66873 4.385638 12.59584 4.417309 Minimum -0.970971 10.08116 101.6297 20.55212 4.093117 11.38458 4.32148 Std. Dev. 0.093945 0.318778 6.76113 1.665446 0.091946 0.408947 0.027091 Skewness 0.065531 -0.255693 -0.219087 0.989543 -1.146481 -0.577478 -0.500595 Kurtosis 1.965479 1.888894 1.876795 2.466317 3.067511 1.90974 2.252894 Jarque-Bera 1.449882 1.994763 1.938117 5.602132 7.016314 3.363452 2.08073 Probability 0.484353 0.368844 0.37944 0.060745 0.029952 0.186053 0.353326 Sum -26.73331 340.6058 3628.534 704.4575 137.3049 388.5089 140.0923 Sum Sq. Dev. 0.273596 3.150205 1417.099 85.98507 0.262076 5.184362 0.022751 Observations 32 32 32 32 32 32 32 4.2 Unit root test In Table 1 , all three stationarity tests (ADF, DF-GLS, and P-P) are demonstrated for log-transformed variables at both the level and first difference form. In each of the three unit root evaluations, it appears that only the urbanization factor is stationary at the level I(0), while the load capacity factor, GDP, GDP squared, private investment in AI, financial globalization, and innovations in technology were non-stationary before we considered their first differences. This mixed sequence of integration encourages us to conduct the assessment now using the ARDL methodology. Table 03 Results of Stationarity test Variables ADF P-P DF-GLS Decision I(0) I(1) I(0) I(1) I(0) I(1) LLCF -0.799 -5.347*** -0.826 -5.354*** -1.475 -4.302*** I(1) LGDP -0.878 -4.841*** -0.953 -4.829*** -1.771 -3.451*** I(1) LGDP 2 -0.614 -5.000*** -0.650 -4.968*** -1.842 -3.423*** I(1) LPAI -0.806 -7.505*** -1.897 -7.403*** -0.933 -5.365*** I(1) LFGOB -2.132 -4.140*** -2.134 -4.090*** -1.943 -4.520*** I(1) LTI -2.015 -5.053*** -2.131 -5.076*** -0.946 -3.765*** I(1) LURBA -8.850*** -1.787 -5.120*** -1.743 -3.781*** -1.462 I(0) 4.3 ARDL bound test To find confirmation of co-integration between the variables, the present research employed an ARDL bounds test approach. The null hypothesis that there is no co-integration is rejected at the 1% significance level, based on the ARDL bound test findings. The critical value has been surpassed by the F test statistic result of 5.6945. Therefore, it may be claimed that the parameters of the model have certain co-integrating associations. According to this investigation, the long-term driving forces consist of urbanization, technological innovation, financial globalization, economic expansion, and private investment in artificial intelligence. These characteristics additionally motivate the system to respond first to a typical stochastic disruption. We conclude that the load capacity factor (LCF) in the United States is affected by differences in all of these variables. Table 04 Results of ARDL bound test Test Statistics Value K F statistics 5.6945 6 Significance level Critical Bounds 10% 5% 2.50% 1% I(0) 1.99 2.27 2.55 2.88 I(1) 2.94 3.28 3.61 3.99 4.4 ARDL Short-run and Long-run After the cointegration had been verified by the bound testing process, we might evaluate the long-term connection among those variables. Table 5 adopts the dynamic ARDL model to demonstrate the short- and long-term effects of LGDP, LGDP2, LPAI, LFGOB, LTI, and LURBA on LLCF in USA. The findings indicate that the load capacity of the US environment seems to decrease with economic expansion over time, but grows with continued expansion of GDP. Urbanization and private investment in AI have been significant contributors to the expansion of load capacity factor, but long-term US LCF is decreased by financial globalization and technological innovation. Our findings demonstrate that the ecosystem gradually loses its natural qualities due to economic development. The US economy is expanding and strongly dependent on energy sources like fossil fuels, which cause ecological damage, therefore the conclusion makes theoretical sense. The results in Table 5 show that the LCF decreases by 3.449% in the long run and by 4.242% in the short run for every 1% increase in GDP. This study's results align with previous studies that established an adverse relationship between GDP growth and load capacity factor. A few studies have concluded that a boost in the GDP has a negative impact on the environment. This includes Xu et al. (2022) for Brazil; Shang et al. (2022) for ASEAN countries; Pata and Balsalobre-Lorente (2022) for Turkey; Khan et al. (2023) in the context of G7 and E7 countries; Akadiri et al. (2022) for India; and Pata (2021) in Japan and the United States. However, for Taiwan, Yeh and Liao (2017) discovered the opposite outcome. They also concluded that Taiwan has come to the point where economic pressures no longer adversely affect the natural world. Likewise, Nathaniel et al. (2020) identified no evidence linking economic expansion to environmental damage in CIVETS (Egypt, Turkey, South Africa, Indonesia, and Vietnam). On the other hand, each unit of growth in GDP2 results in a 1.184% long-term and 0.156% short-term improvement in LCF. Given that the coefficient for LGDP is negative and the coefficient for LGDP2 is positive and both are statistically significant this suggests that environmental pressure diminishes over time, supporting the recently proposed LCC hypothesis for the USA. The coefficients for LPAI indicate a positive correlation with LLCF, implying a 0.015% long-term degradation and a 0.462% short-term increase in LCF for every 1% rise in PAI. Thus, private investment in artificial intelligence in the United States significantly contributes to environmental sustainability. According to Karpovich et al. (2022) executing "green" investment-innovative projects in intelligent manufacturing operated by artificial intelligence plays an integral part in ensuring the ecological security of Russia's local economy. Moreover, based on Platon (2024), eco-investment and artificial intelligence constitute key elements that might accelerate and improve the circular economy. Conversely, LCF is negatively associated with FGOB in both long and short run, and this relationship is statistically significant. These findings suggest that financial globalization has a adverse impact on the USA ecosystem. Specifically, a 1% increase in FGOB decreases LCF by 1.193% in the long run and by 0.502% in the short run. This result is inconsistent with the research of Akadiri and Adebayo (2021), which shows that, in India, pollution levels grow when financial globalization declines, but they drop when it increases. This inference is supported by findings by Xu et al. (2022) and Akadiri et al. (2022), which found a positive correlation between financial globalization and load capacity factor in Brazil and India, respectively. Generally speaking, the globalization of finance symbolizes the advancement of a country's financial sector; a sophisticated financial system would place investments in ecological sustainability ahead of environmentally damaging growth paths (Raihan et al., 2023 ). Similarly, there is a negative correlation between LTI and LLCF, with each 1% increase in TI reducing LCF by 0.127% in the long run and 0.00253% in the short run, and this result is significant at the 1% level. Research by Su et al. (2021) concluded that advances in technology raised emission levels in Brazil. The above findings illustrate that the United States has not yet invested in or implemented green technologies to ensure environmental sustainability. Similarly, Adebayo and Kirikkaleli (2021) verified that technological developments worsen Japan's environmental conditions and raise carbon emissions. This result aligns with the conclusions of Lin and Zhu (2019). That said, it goes contrary to the studies conducted by Khan et al. (2020) and Shahbaz et al. (2020), which found that technological innovation enhances the environmental condition. Additionally, the positive and statistically significant URBA coefficients indicate that both long-term and short-term increases in LURBA negatively affect environmental quality. A 1% increase in URBA raises LCF by 1.182% in the long run and by 17.204% in the short run. These findings suggest that the current urbanization structure in the United States is not conducive to reducing pollution. Research conducted in Singapore by Ali et al. (2017), Saudi Arabia by Raggad (2018), and 19 other countries by Saidi and Mbarek (2017) explored that urbanization improves the sustainability of the environment by lowering emissions of carbon dioxide. But, Wang et al. (2016) found that greater urbanization boosts CO2 emissions. However, the present study’s findings contradict Solarin et al. (2021), who reported that urbanization has no harmful impact on the environmental quality of Nigeria. Table 5 ARDL Long-Run and Short-Run Results VARIABLES LR SR LGDP -3.449***(11.5676) LGDP 2 1.184***(0.52519) LPAI 0.015**(0.03079) LFGOB -1.193***(0.2959) LTI -0.127**(0.16542) LURBA 1.182(5.1537) D.LGDP -4.242**(4.26804) D.LGDP 2 0.156**(0.19304) D.LPAI 0.462***(0.00743) D.LFGOB -0.502***(0.15540) D.LTI -0.00253(0.0074) LURBA 17.204***(2.34298) ECT (Speed Adjustment) -0.684***(0.08539) Constant 10.910***(31.2434) R-square 0.8780 4.5 Robustness Check The DOLS, FMOLS, and CCR methods are additional techniques employed to assess the validity and reliability of the ARDL outcomes. The findings of this study on robustness are presented in Table 6 . The results of robustness testing confirm the findings obtained through ARDL calculations. The economic growth coefficients in the FMOLS, DOLS, and CCR computations are statistically significant at the 1% level and have negative values. It can be concluded from the estimated components that an increase of 1% in GDP causes the load capacity factor (LCF) to fall by 13.532%, 7.325%, and 14.135%, respectively. The higher R-squared values suggest that the estimation was appropriate. A 1% increase in LPAI leads the LCF to grow by 0.013%, whereas an extra 1% in LGDP2 enables the LCF to expand by 0.625% in the FMOLS model. These figures are noteworthy and corroborate the ARDL results displayed in Table 5 . Additionally, a 1% surge in LURBA raises the LCF by 7.428%, whereas a 1% expansion in LTI encourages the LCF by 0.037%. These outcomes also align with the ARDL short and long run estimation. Conversely, a 1% spike in LFGOB causes the LCF average to drop by 1.243%. Similar to the ARDL results, the coefficients LGDP2, LPAI, and LFGOB are significant at the 1% level of significance, whereas LTI and LURBA are significant at the 5% level. Within the DOLS model, an extra 1% in LGDP2, LPAI, LTI, and LURBA raises an average of 0.271%, 0.045%, 0.568%, and 8.671% in LCF. The value of the ARDL results in Table 05 is confirmed by the statistically significant values of these variables. Conversely, a one percent rise in LFGOB leads to an average 1.930% reduction in LCF. Similar to the ARDL conclusions, the coefficient of LFGOB is significant in this particular case. The CCR observations exhibit a similar pattern, except for the LFGOB example. An average of 0.652%, 0.0153%, 0.045%, and 7.796% of LCF are spiked by an additional 1% in LGDP2, LPAI, LTI, and LURBA in the CCR model. Conversely, an extra 1% in LFGOB causes an average 1.236% decrease in LCF. Table 6 Robustness Check Variables FMOLS DOLS CCR LLCF dependent LGDP -13.532***(4.4923) -7.325**(5.8932) -14.136***(6.7150) LGDP 2 0.625***(0.2035) 0.271**(0.1827) 0.652***(0.3053) LPAI 0.013***(0.0121) 0.045**(0.0985) 0.0153**(0.0183) LFGOB -1.243***(0.1619) -1.930***(0.5920) -1.236***(0.1923) LTI 0.037**(0.0722) 0.568*(0.4841) 0.045**(0.0950) LURBA 7.428**(2.2595) 8.671*(3.6591) 7.796***(2.8327) C 44.608**(18.0927) 27.8901**(16.5672) 46.294**(25.9663) R-squared 0.9013 0.9641 0.9005 4.6 Pairwise Granger Causality test The findings of causal linkages across several economic indicators are presented in Table 7 . An F-statistic of 3.38826 and a p-value of 0.0499 indicate that LLGDP does not Granger-cause LLCF. This suggests that we reject the null hypothesis that there is no link between variables at the 5% significance level. Furthermore, p-values below the usual significance threshold confirmed the observation of one-way causation from LGDP2, LPAI, and LTI to LLCF. Therefore, in these circumstances, we reject the null hypothesis that there is no causation. Nonetheless, a strong two-way causal relationship was discovered between LLCF and LGOB as well as between LURBA and LLCF. On the other hand, p-values higher than the traditional significance level for each case demonstrated that there was no significant causal relationship between LLCF and LPAI, LLCF and LGDP, LLCF and LGDP, or LLCF and LTI. As a result, the null hypothesis that there is no causation in these interactions is not successfully rejected. Table 07 Results of Pairwise Granger Causality test Null Hypothesis Obs F-Statistic Prob. LGDP ≠ LLCF 30 3.38826 0.0499 LLCF ≠ LGDP 0.44313 0.647 LGDP2 ≠ LLCF 30 3.4843 0.0463 LLCF ≠ LGDP2 0.44696 0.6446 LPAI ≠ LLCF 30 2.75848 0.0027 LLCF ≠ LPAI 0.2652 0.7692 LFGOB ≠ LLCF 30 6.05754 0.0072 LLCF ≠ LFGOB 0.18985 0.0283 LTI ≠ LLCF 30 3.76786 0.0071 LLCF ≠ LTI 0.87713 0.4284 LURBA ≠ LLCF 30 2.68762 0.0077 LLCF ≠ LURBA 5.37891 0.0114 4.7 Diagnostic Test Table 08 displays the diagnostic examination outcomes. The results demonstrated that the usefulness of all diagnostic procedures is insignificant, and the null hypothesis cannot be rejected. According to the p-value of 0.8027, the Jarque-Bera test confirms that the residuals appear to be normally distributed. The Lagrange Multiplier analysis shows no serial correlation in the residuals, with a p-value of 0.9463. Lastly, the Breusch-Pagan-Godfrey assessment confirms that the residuals do not exhibit heteroscedasticity, with a p-value of 0.3411. Table 8 The findings of diagnostic tests Diagnostic tests Coefficient p-value Decision Jarque-Bera test 0.43948 0.8027 Residuals are normally distributed Lagrange Multiplier test 0.05528 0.9463 No serial correlation exits Breusch-Pagan-Godfrey test 1.1950 0.3411 No heteroscedasticity exists 5. Conclusion The present research comprehensively addresses how the LCF in the USA became influenced by private investment in artificial intelligence (AI), economic expansion, financial globalization, technological innovation, and urbanization between 1990 and 2022. The discoveries propose insightful information on the intricate connections between economic activity and the preservation of the environment. To validate the Load Capacity Curve (LCC) theory, the research makes use of advanced econometric techniques. The findings indicate that while urbanization and PAI reduces the environmental burden, technical advancements, economic growth, and financial integration serve to excerbate these consequences. The results of the stationarity tests reveal that the elements in question exhibit a combination of various degrees of integration and do not exhibit unit root problems. The ARDL bound assessment provides further evidence that these factors are cointegrated, indicating the existence of steady long-term linkages. The ARDL calculations demonstrate a favorable association between GDP growth, TI, FGOB, and LCF and provide short- and long-term support for the LCC hypothesis in the USA. This suggests that environmental damage occurs due to economic expansion when insufficient steps are made to safeguard the environment. On the other hand, the positive correlations between GDP, TI, FGOB and LCF convey that these factors might encourage adverse environmental effects. It is anticipated that financial globalization can provide the required funding for investments in eco-friendly technologies and more productive industrial processes. Similar to this, robust and resilient advances in technology when combined with an openness to trade might foster the creation of novel concepts and the use of greener practices by stimulating healthy competition and granting access to the latest technologies. The validity of the ARDL findings is confirmed by the robustness testing employing FMOLS, DOLS, and CCR, which increases the credibility of the results. Furthermore, the Pairwise Granger Causality tests exhibit significant one-way causal relationships between LLCF and LGDP2, LPAI, and LTI. These relationships emphasize the relevance of how economic shifts, private investments in artificial intelligence, and improvements in green technology impact the dynamics of ecological sustainability in the USA. Therefore, this investigation suggests several legislative solutions aimed at encouraging sustainable economic development in the United States by leveraging financial globalization, technical improvements, and a feasible urban infrastructure. 6. Policy Recommendation In order to tackle the U-shaped correlation discovered in our study between income and environmental sustainability, the United States should adopt a comprehensive and diverse policy strategy. At first, the focus should be on providing green technology and sustainable practices to lower-income areas. This may be done by offering subsidies for the adoption of renewable energy and providing incentives for eco-friendly enterprises. As income levels increase, it is necessary to enhance laws in order to reduce environmental degradation caused by higher levels of consumption and industrial operations. This entails implementing rigorous emissions regulations, advocating for energy conservation, and allocating resources towards sustainable infrastructure. To promote sustainability among high-income groups, policymakers can incentivize investments in clean energy through tax benefits, implement carbon pricing systems, and allocate funds for new environmental technology. Furthermore, it is imperative to strengthen education and awareness initiatives on sustainable behaviors among individuals of all income brackets in order to cultivate a societal ethos of environmental accountability. It is imperative for federal and state governments to cooperate in order to guarantee the efficient implementation of these policies, while also customizing them to suit the specific requirements of each region. Through the implementation of this all-encompassing strategy, the United States may utilize economic expansion to enhance environmental results and attain enduring sustainability. In order to maximize the beneficial effects of private investment in AI on environmental sustainability, the United States should implement specific and focused regulatory initiatives. Firstly, offer tax incentives and subsidies to private firms that invest in AI technologies that improve environmental sustainability, such as smart grids, precision agriculture, and predictive maintenance to minimize waste and emissions. Facilitate the formation of collaborations between the public and commercial sectors to expedite the implementation of sustainable solutions powered by artificial intelligence. This will ensure that even small and medium-sized firms have the opportunity to benefit from these advancements. Enforce policies that promote openness and accountability in the use of AI technology to mitigate unanticipated adverse environmental effects. Increase research and development funding for artificial intelligence (AI) programs that specifically target sustainability, with an emphasis on promoting innovation in areas such as climate modeling, resource management, and energy efficiency. Furthermore, advocate for the use of artificial intelligence (AI) into environmental monitoring and enforcement endeavors to enhance adherence and effectiveness. Advocate for workforce development projects that focus on cultivating proficiency in artificial intelligence and environmental sustainability. This will ensure the availability of a highly qualified labor force capable of driving progress in these areas. To leverage technology developments and establish itself as a frontrunner in the green economy, the United States may provide a favorable climate for private investment in AI, therefore promoting significant strides in environmental sustainability. In order to counteract the negative effects of technical innovation and financial globalization on reducing the load capacity factor, the United States should implement a strategic policy framework. Firstly, establish policies that promote the use of sustainable technical innovations, with a focus on optimizing resource utilization and reducing environmental impacts. Offer incentives to encourage enterprises to create and use environmentally friendly technologies that increase the ability to handle workloads without using up resources. Facilitate responsible financial globalization by implementing regulations that guarantee investments uphold sustainable practices and refrain from exploiting natural or human resources. Enhance global collaboration to harmonize worldwide financial transactions with sustainability objectives, guaranteeing that overseas investments and technology transfers make a positive contribution to environmental sustainability. Promote and fund research and development in sustainable technologies and practices, encouraging innovative solutions that achieve a balance between economic growth and environmental stewardship. In addition, improve education and training programs that specifically target sustainable practices and the environmental consequences of globalization, equipping the workforce to actively participate in and promote these endeavors. Through the incorporation of these policies, the United States can effectively tackle the difficulties presented by technical advancement and financial globalization, guaranteeing long-term growth and safeguarding the nation's ability to support future generations. References Ahmad S, Raihan A, Ridwan M (2024) Role of economy, technology, and renewable energy toward carbon neutrality in China. 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Introduction","content":"\u003cp\u003eThe destruction of the natural environment is one of the most pressing issues facing the modern world today (Raihan et al.,2024; Li et al.,2021; Liu et al., 2021). This is due to its adverse effects on the overall economy, biodiversity, the atmosphere, human health, the quality of the air, and assets including groundwater, soil, and forests (Rehman et al., 2021). Globally, maintaining economic growth and reducing climate change now depend on reducing CO2 emission level and maintaining ecological integrity (Raihan et al., 2022). Moreover, a great deal of this emission originates from a small number of nations (Magazzino et al., 2020). Even though the US economy has been expanding for over three decades, the nation is dealing with major environmental problems (Koondhar et al., 2018). As of right now, China is the nation with the fastest pace of economic growth, with the United States standing in second (He and Richard 2010). Even though China accounts for 28% of global CO2 emissions, the USA is responsible for 16%, the EU for 11%, India for 6%, and other countries for 39%. However, as China has a population four times that of the USA, the USA has higher CO2 emissions per capita than China (Koondhar et al., 2018). Moreover, according to 2020 year-end data (WorldBank, 2021) the USA produces almost 14%.These alarming figures underscore the relevance of the present research from a US perspective. Significant outcomes can be obtained by utilizing relevant factors including financial globalization, technical innovation, and private investment in AI. The research findings can be implemented by policymakers to ensure environmental sustainability in the United States. The effect of multiple socio-economic and technical variables on carbon emissions has been a focus of numerous researches (Orhan et al., 2021; Su et al., 2021; Zhang et al., 2021; Guloglu et al.,2023 and Raihan et al.,2023). Even if carbon emissions make up a sizable amount of greenhouse gases, Akinsola et al. (2021) claimed that carbon emissions are insufficient to accurately depict and assess total environmental damage. On the other hand, the ecological footprint was first proposed by Rees (1992), and it was subsequently emphasized by Galli et al. (2012) as one of the most extensive economic-ecological indicators for evaluating environmental damage. In spite of this, not much research has been done on the Load Capacity Curve (LCC) concept. Therefore, there is a lack of knowledge in the literature about the LCC hypothesis's validity in emerging economies like the USA. To close this gap, this research assesses the LCC hypothesis's relevancy to the USA. According to Siche et al. (2010), the load capacity factor offers a more precise ecological assessment. The load capacity factor shows how strong or able a country is to support its citizens according to their modern lifestyles (Xu et al., 2022). Therefore, an ecosystem is considered to be unsustainable when the LCF is less than 1, and sustainable when the LCF is greater than 1 (Pata et al., 2021).\u003c/p\u003e \u003cp\u003e We have recently witnessed revolutionary developments in several industries as Artificial Intelligence (AI) has become increasingly integrated and the environment field is no exception. The issue of global sustainable development can be resolved somewhat by the progress of AI technologies. Moreover, the implementation of AI can lower emissions to the environment (Shang et al.,2024). AI boosts Chinese industries' environmental sustainability and dramatically lowers the intensity of pollutant emissions (Cheng et al., 2024). Artificial intelligence-driven commercialization is expected to reach \u003cspan\u003e$\u003c/span\u003e3.9 trillion in 2022, up from \u003cspan\u003e$\u003c/span\u003e1.2 trillion in 2018, which marked a 70% growth from 2017 (Brown, 2013; Fatorachian and Kazemi, 2018; Richards et al., 2019). The public sector's contribution to AI has been expanding over the past few decades, as seen by the \u003cspan\u003e$\u003c/span\u003e3.2\u0026nbsp;billion in investments made by the U.S government in 2022 (JEC,2023). Additionally, in almost 65% of AI-enabled environmental initiatives, mathematical models are used. All environmental professionals are likely to gain numerous advantages from AI tools (Konya and Nematzadeh, 2024). It can assist policymakers in formulating scientifically grounded strategies and plans for the sustainability of the environment (Asadnia et al.,2014; Asadnia et al.,2017; Farahnakian et al., 2011). AI-driven technologies are crucial for ecological resource conservation as they facilitate the monitoring and preservation of natural habitats, animal populations, and ecosystems (Krishnamoorthy and Sistla, 2023). To discover biodiversity hotspots, monitor endangered species, and identify threats to the environment like deforestation, poaching, and pollution, machine learning algorithms can examine satellite images, sensor data, and ecological parameters (Krishnamoorthy and Sistla, 2023). Stakeholders can safeguard biodiversity and ecosystem services by implementing focused interventions and making educated decisions by utilizing AI for environmental monitoring and conservation (Sistla and Konidena, 2023).\u003c/p\u003e \u003cp\u003eThere are several ways in which the process of economic growth might result in environmental damage (Kartal et al., 2022). Growth in the economy comes with a substantial consumption of energy, natural assets, and production inputs, which initially pollutes the environment and puts more strain on it (Nurgazina et al., 2022). With time, the pressure on the ecosystem declines, and as environmental knowledge and demand grow, higher income levels help to improve environmental quality (Pata et al.,2023). On the other hand, in Bangladesh and Indonesia, Bakirtas et al. (2023) discovered a reverse U-shaped link between GDP and LCF. In 2024, the United States accounted for 13.3% of global GDP and for around 4.21% of the global populace (World Bank, 2024). Meanwhile, almost 16% of global CO2 emissions came from 5,416 MT of emissions in the United States (BP, 2020). Since the US is one of the major emitters of greenhouse gases into the atmosphere, it bears some of the blame for the climate crisis and global warming. The catastrophic consequences of the United States' roughly 1.0 degree Celsius climate change are already worrisome, affecting the most vulnerable members of the population with a climate-fueled disaster that caused fatalities, deteriorating health, a low standard of living, and even the destruction of the earth's ecosystem (Zhang et al.,2023). The majority of research has shown that the growing population causes more environmental damage (Voumik and Ridwan, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Khan et al., 2021; Pham et al., 2020; Menz and Welsch, 2012). Financial globalization (FGOB) considers characteristics such as international assets and liabilities, FDI, investment portfolios, and related laws to assess how far a nation has incorporated into the global financial system. As a result, FGOB is a noteworthy measure of financial progress (Dhingra, 2023; Wang et al., 2023). With the progress of financial globalization, foreign direct investment is increasing globally. The most current UNCTAD (2020) showed that from \u003cspan\u003e$\u003c/span\u003e1.3 trillion in 2018 to \u003cspan\u003e$\u003c/span\u003e1.5 trillion in 2019, there was a 3.0% rise in worldwide FDI inflows. Scholars like Furceri et al. (2019), Usman et al. (2019), Raghutla and Chittedi (2020), Obstfeld (2021), Gungor et al. (2021), and Awosusi et al. (2022) characterize financial globalization as the convergence of global monetary systems into a single market. Through the optimization of resources, clean energy, garbage disposal, prevention of pollution, and monitoring of the atmosphere, technological innovation can slow down the decline of the environment (Ha 2022; Vyas et al. 2022; Ramzan et al. 2023).\u003c/p\u003e \u003cp\u003eConsequently, our investigation contributes significantly to the collection of current literature in several ways. First of all, from a U.S. viewpoint, it addresses the largely unexplored field of private investment in AI, which makes it distinctive. The experimental research has presented consistent results concerning the correlation between load capacity factor (LCF) and private AI investment (PAI). This study aims to clarify the linkages between LCF and PAI in light of the situation described above, offering additional relevant data for designing green policies. Second, the study makes use of unique PAI data that is categorized as Estimated Investment in AI (US\u003cspan\u003e$\u003c/span\u003e) and is derived from Our World in Data. Within the framework of the USA's load capacity factor (LCF), this analysis focuses on the trends and key research areas of private investment in artificial intelligence (AI), financial globalization, technical innovation, economic development, and urbanization. Analyzing the LCF within the context of the USA will offer fresh perspectives to scholars exploring the issue and establish a noteworthy contribution to the body of understanding. As far as we are aware, our investigation is the first to conduct a detailed review of the literature on the LCF, enabling us to embark on the following research goals: What effect do PAI and FGOB have on the USA's LCF? In what ways can independent and dependent variables interact spontaneously? Furthermore, how do TI, GDP, and URBA affect the LCF? The significance of this research lays in the fact that private investment in artificial intelligence and financial globalization has not been extensively studied in other studies. By recognizing these elements, policymakers and strategy developers might be able to more effectively promote environmentally responsible behavior. More research in this area is essential to building a pleasant and sustainable environment, particularly in light of increasing interest in green cities and public awareness of ecological issues. The effects of GDP, PAI, FGOB, TI, and URBA on the LCF are examined in inquiry using ARDL methodologies from 1990 to 2018. Additionally, the robustness of the outcomes was checked as well using the FMOLS, DOLS, and CCR techniques. This study delivers valuable insights for legislators in the USA and other nations to achieve the SDGs while simultaneously promoting sustainable economic growth and increasing the quality of the environment (as evaluated by the LCF) by adopting an integrative approach to the issue.\u003c/p\u003e \u003cp\u003eThe paper examines the body of research on the chosen determinants in the second part. The information, theoretical framework, empirical model development, and estimating methods used to conduct the study are all covered in detail in the \"Methodology\" section. \"Results and Discussion,\" the fourth part, offers an extensive discussion of the model's findings. The fifth and last part summarizes the analysis and suggested strategies of action.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe impact of financial globalization, advancements in technology, and economic expansion on the load capacity factor (LCF) has been the subject of several empirical studies. While many analyses examined the ARDL model, the majority of papers focused on how trade openness, urbanization, and green energy usage affect environmental quality. Others have focused on analyzing the connection between trade openness, globalization, economic growth, and LCF. Previous studies on the concept of ecological degradation in the context of the USA have not yet been extensively conducted as this is a relatively new field. However, the investigation used some prior studies that assisted with the selection of variables and methods. This section will cover a few of these inquiries.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Economic Growth and Load Capacity Factor\u003c/h2\u003e \u003cp\u003eThe relationship between economic development and environmental sustainability has been the subject of several studies. Many believe that as the economy grows there will be an increase in emissions of CO2. However, things become more complicated when we include load capacity in addition to CO2 emissions as environmental quality criteria. People's goals for monetary progress encourage them to use all available energy resources, which has an economic effect of producing emissions (Panel et al., 2011). In addition, when business activity expands to achieve incredible growth, natural resource depletion takes place (Teng et al.,2024). Thus, the reduction of biocapacity, biodiversity, and the load capacity factor in different economies and areas can be linked to greenhouse gas emissions and resource depletion for income development (Zhang et al., 2022). In Pakistan, Ali et al. (2023) performed an experiment using a \"dynamic autoregressive distributed lags model\" and a unique approach called \"Kernel-based regularized least squares (KRLS).\" They discovered an unexpected negative correlation (-0.270) between the load capacity factor and GDP growth over the long run. According to Pata (2021), growth in GDP significantly degrades the environment in a manner that cannot be balanced by renewable energy sources or increased healthcare costs in the United States. Similar research (Fareed et al. 2021; Huilan et al.,2022; Shang et al. 2022; Abdulmagid Basheer Agila et al. 2022; Jin and Huang.,2023; \u0026Ccedil;AMKAYA and KARAASLAN.,2024) that used the LCF as the dependent variable discovered that increases in economic growth exhibited a negative consequences on the LCF indicating the destruction of the ecosystem. By examining the consequences of GDP, Li et al. (2023) seek to understand how the next eleven countries improved their LCF between 1990 and 2018. The long-term findings indicate that reliance on economic growth reduced LCF. However, Ullah et al. (2023) insist that while there is no short-term effect, a growth in economic complexity has a positive long-term influence on the LCF. On the other hand, a U-shaped link between income and the quality of the environment was observed by Guloglu et al. (2023), confirming the validity of the LCC theory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Private Investment in AI and Load Capacity Factor\u003c/h2\u003e \u003cp\u003eThe increasing prevalence of artificial intelligence (AI) technology in our daily lives has extensive political and socioeconomic implications. Both officially and privately funded AI research and applications are encouraged heavily (Brandusescu, 2022). Negi (2018) focuses on the flow of investment in artificial intelligence from the top three major nations in the field China, India, and the United States. The research illustrates the steps that the government has taken to incorporate artificial intelligence into its present ecosystem, which is supported by the private sector. The t-test demonstrates a significant relationship between annual investment and trend analysis, which suggests that the AI industry is growing quickly. Vietnam is seeing a relatively small amount of misdirected AI investment. Vietnam excessively remains significantly behind other Southeast Asian nations, necessitating both governmental and private investment in this sector (Pham et al.,2024). Artificial intelligence (AI) investment from the private sector has an enormous impact on the environment, positive as well as negative. AI helps with the preservation of natural assets, controlling energy consumption, ecological safeguarding, pollution control, agriculture, and other areas, all of which are critical to attaining environmental sustainability (Kumari and Pandey,2023). In a similar vein, Habila et al. (2023) indicate that the use of AI improves human capacity to manage climate change to achieve sustainability while utilizing natural resources. On the other hand, Okengwu et al. (2023) reveal that increased usage of AI in agriculture results in increased carbon emissions that affect humanity and the natural world. Green AI can boost productivity and alleviate its negative effects on the environment (Pachot and Patissier, 2022). Since private investment in AI usually has a negative impact on the natural world, government officials need to advocate for increased private investment, particularly in green AI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Financial Globalization and Load Capacity Factor\u003c/h2\u003e \u003cp\u003eFinancial globalization is the uncontrolled and free movement of financial resources across national boundaries (Kose et al. 2009).Both positive and negative effects of financial globalization on the load capacity factor are apparent in emerging countries. In the instance of India, Akadiri et al. (2022) demonstrated that FGOB is positively connected with the LCF both in the short and long run. According to Raihan et al. (2021), the short- and long-term consequences of FGOB are favorable for the load capacity factor. By taking into account financial globalization over the years 1980\u0026ndash;2021, Ozcan et al. (2024) aim to look at how Germany\u0026rsquo;s environmental quality is affected. Through the use of advanced quantile-based methods, they highlight how financial globalization boosts the quality of the environment. Moreover, using panel econometric approaches, Wang et al. (2022) scrutinized the most recent yearly data set that included 31 OBOR countries from 1996 to 2018. The environment deteriorates due to financial globalization, according to the findings. Many scholars also agreed that FGOB slows down environmental damage by increasing the LCF (Jin et al.,2023; Xu et al.,2022;Pata et al.,2021;Yang et al.,2023). However, in the case of Bangladesh, the effects of financial globalization on the ecosystem are multifaceted and rely on several variables, including clean FDI and the use of renewable energy (Murshed et al. 2021). From 1990 to 2017, Kihombo et al. (2022) checked out the link between environmental impact and financial globalization in some West Asian and Middle Eastern (WAME) countries. The long-run predictions from continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) tests demonstrate that, through lowering the ecological footprint, financial globalization contributes a major part in promoting environmental sustainability. Several outcome was also observed by (Awosusi et al.,2022: Ulucak et al.,2020; Tahir et al.,2021) and they reveal that a positive effect of FGOB on the LCF, indicating that the enhancement of environmental quality. In light of these outcomes, it is essential to determine if financial globalization provides the USA with a comparable opportunity to boost its load capacity factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Technological Innovation and Load Capacity Factor\u003c/h2\u003e \u003cp\u003ePrevious research has mostly overlooked technological innovation (TI), and it has been found to have both positive and negative consequences. The continuous improvement in the degree of innovation has rendered policymakers as well as scholars recognize the significance of technological innovation in preventing environmental deterioration (Du et al., 2022; Haldar and Sethi, 2022). Numerous studies have been performed to analyze the fundamental connections between LCF and technological progress. The MMQR approach is used by Jahanger et al. (2024) to analyze the consequences of technological innovation on LCF between 1994 and 2018. The results of the study demonstrate that, in the top 10 SDG nations, TI has an adverse and substantial effect on lowering LCF. To examine how technological innovation affects environmental quality in China, Kartal and Pata (2023) consider environmental indicators such as carbon dioxide (CO2) emissions, ecological footprint (ECF), and load capacity factor (LCF). The findings exhibit that whereas TIN reduces LCF at middle quantiles, it increases CO2 emissions and ECF at higher quantiles. Several analysis also found that technological innovation is hazardous for the ecosystem (Raihan et al.,2024;Su et al.,2023; Adebayo et al.,2022). On the other hand, Wang et al. (2020) researched N-11 economies between 1990 and 2017, utilizing the unit root test, augmented mean group, and common correlated effect mean group proposed by Pesaran (2007) serve as the foundation for the empirical estimations. The outcomes illustrate that technological innovation has a negative relationship with carbon emissions, which boosts the quality of the environment. Furthermore, Kihombo et al.(2021) assessed the impact of TI on environmental quality in West Asian and Middle Eastern countries and demonstrated that TI strengthens the natural world. Similarly to this, Rafique et al. (2020) provided evidence that technical advancement lowers pollution in the BRICS countries. Multiple studies as well as showcase the positive consequences of technological innovation on maintaining economic sustainability (Mehmood et al.,2023; Khan et al.,2023; Anwar et al., 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Urbanization and Load Capacity Factor\u003c/h2\u003e \u003cp\u003eThe goal for individuals shifting from rural to urban areas is to lead ordinary lives while working in industries that generate revenue (Ruel et al., 2008). Furthermore, the concept of the smart city, which advocates for energy from both renewable and nuclear sources, has emerged as the primary goal of industrialized and modern society (Chenic et al., 2022). The ARDL approach was utilized by Raihan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to investigate cointegration and both short- and long-term dynamics using time series data from 1971 to 2018. The result illustrates that urbanization lowers Mexico\u0026rsquo;s load capacity factor and, therefore lowers the quality of the environment. Urbanization has a detrimental impact on the dynamics of load capacity, which accelerates environmental deterioration (Teng et al.,2024). Using Cross-Sectional ARDL estimators and AMG estimators, Shah et al. (2023) performed a study in the top 15 nations that produce natural gas and discovered that urbanization has a significant impact on environmental damage. Similarly Raihan et al.(2024) and Caglar et al.(2023) also concluded that urbanization is harmful for the ecosystem. However, the relationship involving urbanization and ecological sustainability is investigated by Fang et al. (2024) using the frequency domain causality technique and the ARDL estimator. The load capacity factor curve theory is supported in Thailand as the ARDL estimator finding shows that urbanization reduces LCF. Zhu et al. (2018) came to the same conclusion that URB makes the natural world better. Additionally, Xu et al. (2022) assessed how urbanization affected the load capacity factor in Brazil between 1970 and 2017. Surprisingly, the outcome of the ARDL approach revealed that the LCF is not affected by urbanization in Brazil. Similar findings have been reported by Chen et al. (2022) using CCEMG and AMG tests for the years 1990\u0026ndash;2019 and Haseeb et al. (2018) using FMOLS from 1995 to 2014, indicating that URB had no appreciable effect on the environment quality for the BRICS countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Literature Gap\u003c/h2\u003e \u003cp\u003eThe relationships between load capacity factor, financial globalization, the private investment in AI, technological development, economic expansion, and urbanization in the USA have not, as far as we are aware, been investigated. Although researchers have looked into these areas on their own, they haven't consistently merged their discoveries. Previous research efforts demonstrated several shortcomings, especially an absence of comprehensive analyses of the connection between PAI and LCF in the USA. Private sector AI investments could aid agriculture, promote renewable energy, mitigate risks like oil spills, and develop sustainable practices, thereby reducing global warming risks. All of these aspects constitute PAI an entirely new field to investigate for the USA perspective. To cover up these deficiencies, this study explores the link between PAI and the environment utilizing strong statistical approaches such as ARDL, FMOLS, DOLS, and CCR procedures. Through a review of these procedures, the USA might find out if harnessing technical innovation, monetary integration, and business growth can offer the possibility of elevating its load capacity factor and bringing it into line with broader global shifts toward improved environmental sustainability. The implications of the load capacity factor in this area have not yet been the focus of inquiry. As a result, this investigation considers these components as essential to long-term environmental sustainability. By assisting stakeholders and lawmakers in establishing policies that are customized to the distinctive ecological and socioeconomic dynamics of the United States, this analysis advances sustainable development.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Data and Variables\u003c/h2\u003e\n \u003cp\u003eThe ongoing research analyzed data to check out how technical advancements, financial globalization, GDP, urbanization, and private investment in AI influenced the USA\u0026apos;s LCF between 1990 and 2019. The United States gathered attention because of its sustainability concerns and data accessibility. The World Development Index (WDI) is the source of the GDP and URBA figures. Here, we consider the LCF as an endogenous variable that derives from GFN and is utilized as a substitute for ecological sustainability. Our World in Data is the same source from which PAI and TI information was collected. Conversely, the FGOB info is adopted from the KOF Globalization Index. In addition, we selected FGOB, TI, and PAI as our investigation\u0026apos;s policy variables.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 01\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSource and Description of Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogarithmic Form\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit of Measurement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoad Capacity Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLLCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGha per person\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGross Domestic Product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (current US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Investment in AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimated Investment in AI (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOur World in Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGOB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Globalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLFGOB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobalization Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKOF Globalization Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnological Innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatent applications, residents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOur World in Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLURBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban population (% of total population)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Theoretical Framework\u003c/h2\u003e\n \u003cp\u003eThe most important instrument in the realm of the environmental field is the load capacity curve (LCC), which offers fascinating details on the complex links between ecological sustainability, economic success, and progress in humanity. This is significant as it illustrates the balance\u0026mdash;or absence between the planet\u0026apos;s capability of restoring its natural resources (biological capability) and the utilization of human capital (ecological footprint). Since biocapacity and EF are incorporated in the denominator of the LCF, a greater LCF is symbolic of a healthier environment (Pata and Kartal, 2023).\u003c/p\u003e\n \u003cp\u003eThe LCF offers a more comprehensive ecological assessment by contrasting biocapacity and ecological footprint (Dogan and Pata, 2022). Furthermore, the LCC highlights the interconnectedness of the world\u0026apos;s biological issues as claimed by Wu et al. (2023), including climate change, resource scarcity, and loss of biodiversity. It is believed that the LCC has a U-shaped connection, with GDP constituting as the main driver. According to Pata and Tanriover (2023) and Pata and Ertugrul (2023), there are distinct trends in the implications of GDP on the environment, suggesting a U-shaped curve connection. The awareness that resource usage grows in tandem with economic expansion and developments in personal wealth is highlighted by this relationship as a crucial aspect of ecological sustainability (Degirmenci \u0026amp; Aydin, 2024).\u003c/p\u003e\n \u003cp\u003eFinancial globalization promotes cross-border economic activity, which elevates national manufacturing and, consequently, exacerbates environmental degradation (Xu et al.,2022). Additionally, the rise of cities and industries is fostered by urbanization and financial globalization, which increases the demand for energy to support the expansion of infrastructure and industrial processes, both of which harm the atmosphere (Ahmed et al., 2021; Tufail et al.,2022; Yang and Khan,2022). The results by Caglar et al.(2023) validate the load capacity curve (LCC) concept by demonstrating that monetary eventually becomes an ecologically beneficial factor. As was previously indicated, there may be a range of linkages between the components, including GDP growth, technical innovation, and private investment in AI, urbanization, financial globalization, and load capacity factor. To improve knowledge of previous research, we have created the following Eq.\u0026nbsp;(1) for LCC theory:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:Load\\:Capacity\\:Factor=f\\left(GDP,\\:{Y}_{t}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHere, Y\u003csub\u003et\u003c/sub\u003e is a variable for additional parameters impacting the load capacity factor, while GDP is a variable for income in Eq. (1). Eq. (2) seeks to provide a deeper comprehension of the factors impacting the load capacity factor by including additional relevant variables such as urbanization, financial globalization, private investment in AI, and economic growth.\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:LCF=f\\left(GDP,\\:PAI,FGOB,\\:TI,\\:URBA\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)\\:$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe load capacity factor in Eq.\u0026nbsp;(2) is represented by LCF, whereas the terms financial globalization (FGOB), technological innovation (TI), urbanization (URBA), and private investment in artificial intelligence (PAI) are introduced to symbolize particular principles. The econometric explanation of Eq.\u0026nbsp;(3) is given above.\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:{LCF}_{it}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}GD{P}_{it}++{\\alpha\\:}_{2}{PAI}_{it}+{\\alpha\\:}_{3}{FGOB}_{it}+{\\alpha\\:}_{4}{TI}_{it}+{\\alpha\\:}_{5}{URBA}_{it}\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eEquation (4) illustrates the variables\u0026apos; logarithmic values. It increases understanding and facilitates the formulation of conclusions based on statistics by breaking down complicated connections into more straightforward linear forms. Logarithmic scales can manage data of various sizes and assist with heteroscedasticity when broad ranges need to be minimized.\u003c/p\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:{LLCF}_{it}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}LGD{P}_{it}+{\\alpha\\:}_{2}{LPAI}_{it}+{\\alpha\\:}_{3}L{FGOB}_{it}+{\\alpha\\:}_{4}L{TI}_{it}+{\\alpha\\:}_{5L}{URBA}_{it}\\:\\left(4\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eHere, the research\u0026apos;s coefficients are displayed in the parameter range of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{6}\\)\u003c/span\u003e\u003c/span\u003e in Eq. (4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Econometric Framework\u003c/h2\u003e\n \u003cp\u003eThis investigation deployed the ARDL technique for data estimation to explore the link between LCF and variables like GDP growth, PAI, FGOB, TI, and URBA in the USA. We additionally utilized the FMOLS, DOLS, and CCR approaches to guarantee robustness. To ensure stationarity, the unit root tests (ADF, P-P, and DF-GLS) were performed at the beginning of the study. Because of the nature of the time series data, the ARDL bound test was then implemented. The ARDL (both short run and long run) estimate was then carried out. Ultimately, after an elaborate estimating procedure, we determined which econometric model was the most efficient and trustworthy.\u003c/p\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Unit Root test\u003c/h2\u003e\n \u003cp\u003eTo ensure consistency in information, a regression test was conducted to eliminate unit roots across all variables. This is important because factors involving unit roots or non-stationary data must assist in explaining a greater proportion of the results to prevent the drawing of incorrect conclusions (Nelson and Plosser, 1982; Engle and Granger, 1987). It is essential to use a unit root test to prevent incorrect regression. The stationary nature of the regression variables is confirmed by differences and stationary processes (Raihan and Tuspekova,2022; Raihan et al., 2022). The empirical research\u0026apos;s findings indicate that before applying cointegration approaches, the integration sequence must be determined (Sahoo and Sethi, 2022). A time series\u0026apos; stationarity or non-stationarity is calculated by Voumik and Ridwan (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), since it is critical in identifying non-stationary data that might produce inaccurate results. To observe the stationarity within the data set, this research adopted the Dickey Fuller-Generalized Least Squares (Elliot et al., 1992) unit root test, the Philips Perron (Philips and Perron, 1968), and the Augmented Dickey-Fuller (Dickey and Fuller, 1979) unit root examination. Due to its ability to control serial autocorrelation, the ADF technique has become more popular (Dickey and Fuller, 1981). Compared to the Dickey-Fuller (DF) approach, the ADF technique is more robust and applicable to more sophisticated procedures (Fuller, 2009). The objective of employing the unit root test was to approve that no variable surpassed the integration order, thus bolstering the methodological soundness of the ARDL simulation (Raihan,2024).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Autoregressive Distributive Lag Model\u003c/h2\u003e\n \u003cp\u003eThe ARDL test was developed by Pesaran et al. (2001) and widely utilized due to its robustness and adaptability in handling different degrees of variable integration. If the indicators are integrated at the I(0) or I(1) level, the ARDL Bounds testing method can be applied, in contrast to traditional cointegration assessments. This approach is beneficial even with a small sample size since it produces dependable and consistent estimates even when there are only a limited number of data point\u0026rsquo;s available (Ridzuan et al.2023;Pattak et al.,2023). The longer-period relationship between LCF, GDP, PAI, FGOB, TI, and URBA is shown by Formula (8). This method was created to assist in defining ARDL Bounds:\u003c/p\u003e\n \u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\u003cimg 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\" style=\"width: 850px;\"\u003e\u003cbr\u003eWe compare the alternatives, which state that there is evidence of cointegration, with the null hypothesis, which states that there is no cointegration. It is possible to conclude that the variables are long-term correlated if the F-statistics are larger than the highest critical value for rejecting the null hypothesis. If the F-statistic is smaller than the lowest allowable value, the null hypothesis is accepted. If the F-statistics are seen to be between the lowest and maximum limits, the test is considered inconclusive (Raihan et al.,2023). Equations 5 and 6 reveal the null and alternative hypotheses:\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equf\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e$$\\:{H}_{0}={\\sigma\\:}_{1}={\\sigma\\:}_{2}={\\sigma\\:}_{3}={\\sigma\\:}_{4}={\\sigma\\:}_{5}={\\sigma\\:}_{6}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(5\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equg\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e$$\\:{H}_{1}={\\sigma\\:}_{1}\\ne\\:{\\sigma\\:}_{2}\\ne\\:{\\sigma\\:}_{3}\\ne\\:{\\sigma\\:}_{4}\\ne\\:{\\sigma\\:}_{5}\\ne\\:{\\sigma\\:}_{6}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(6\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe signs, \u0026quot;H0 and H1\u0026quot; denoted the null hypothesis and the alternative hypothesis respectively.\u003c/p\u003e\n \u003cp\u003eThis study assessed the error correction model (ECM) after determining the long-term connections to investigate the short-term behavior of the independent variables and the short-term adjustment rate toward the long-term rate (Luqman et al., 2021). To do this, the ECM is included in the ARDL structure, as shown in Eq.\u0026nbsp;(6)\u003c/p\u003e\n \u003cdiv id=\"Equh\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\u003cimg 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\" style=\"width: 847px;\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eHere, the notion ℓ is the rate of adjustment.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 Robustness Check\u003c/h2\u003e\n \u003cp\u003eThis study scrutinized the FMOLS, DOLS, and CCR technique to represent the long-run impact of GDP, PAI, FGOB, TI, and URBA on LCF in order of evaluating the stability within the ARDL long-term estimation. When there is evidence of series cointegration, FMOLS and DOLS can be utilized. However, the biggest advantage of the DOLS estimation is its ability to present different levels of integration of discrete elements inside the cointegrated framework (Pesaran, 1997; Raihan and Tuspekova, 2022). The FMOLS technique was developed by Hansen and Phillips(1990). When addressing cointegration and its influence on autocorrelation and endogeneity in the explanatory variables, the FMOLS technique modifies the least squares approach (Pattak et al.,2023). By estimating the dependent variable on explanatory factors in levels, leads, and lags, the DOLS technique successfully permits individual variables in the cointegrated outline to be integrated when a mixed order of integration occurs (Raihan et al.,2023).The CCR technique was developed by Park (1992) and merely utilizes the static part of a lagged model to convert data. In a cointegrating system, the CCR ensures that data extracted from explanatory variables on unobserved heterogeneity will show at zero frequency. As a result, the CCR approach produces chi-square and arithmetically effective approximation assessments devoid of any undesirable aspects. We therefore use the FMOLS and DOLS estimators to determine elasticity over the long run. What follows is Equivalent to the FMOLS equation is Exhibit 8.\u003c/p\u003e\n \u003cdiv id=\"Equi\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\u003cimg 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\" style=\"width: 620px;\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eHere, the longer-period flexibility is assessed using the FMOLS and CCR coefficients and t shows the time-varying trend.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.4 Pairwise Granger Causality test\u003c/h2\u003e\n \u003cp\u003eThe concept of a causality analysis aims to determine whether or not previous changes in a factor are responsible for the current observation, as theoretical correlations may not hold in practice due to certain elements that may not be clearly described in theory (Voumik et al.,2023). This work employs the Pairwise Granger causality test which is a statistical concept of causation based on a prediction that offers several advantages over other time-series research methodologies (Winterhalder et al. 2005). To say that X is causally related to Y would be to say that the sum of X\u0026apos;s prior and current values deviates substantially beyond 0. Y and X causality are subject to the same laws; if the results deviate from zero, it indicates the presence of causation on both sides. The analysis used the paired Granger causality test introduced by Granger (1969), to ascertain if there prevailed a short-term causal link between the components. Eq.\u0026nbsp;(9) shows that Xt and Yt are causally related.\u003c/p\u003e\n \u003cdiv id=\"Equj\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e$$\\:E\\left({Y}_{t+h}|{J}_{t,}{X}_{t}\\right)=E\\left({Y}_{t+h}|{J}_{t}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(9\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHere, Jt notation is used for the sets of information gathered from all of the outcomes up to a certain point of time (t).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.5 Diagnostic test\u003c/h2\u003e\n \u003cp\u003eSeveral diagnostic tests were implemented in this research to confirm the data\u0026apos;s heteroscedasticity, serial correlation, and normality. The Lagrange Multiplier (LM) test, the Jarque-Bera test (Jarque and Bera, 1987), and the Breusch-Pagan-Godfrey test (Breusch and Pagan, 1979) serve as essentials for validating model assumptions and guaranteeing the robustness of results in time series analysis. Since many econometric models assume normally distributed errors for successful inference, the Jarque-Bera examination checks the normality of residuals, a vital phase in the process. By detecting serial correlation in residuals, the Lagrange Multiplier test makes sure that errors do not correlate over time, which might result in misleading and biased estimations. The heteroscedasticity, or non-constant variance of residuals, is verified using the Breusch-Pagan-Godfrey test, which can lead to inaccurate standard errors and estimates.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Result and Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Summary Statistics\u003c/h2\u003e \u003cp\u003eThe summary statistics for the variables we investigated with 32 observations are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e02\u003c/span\u003e below. The descriptive data for the USA for the following seven variables are provided (LLCF, LGDP, LGDPSQ, LPAI, LFGOB, LTI, and LURBA). As can be seen in the table, all of the variables we chose had a positive mean except LLCF, and the mean was the highest for LGDPSQ. Furthermore, the estimated standard deviations of each variable are quite small, implying that the data points are centered around the mean and have minimal periodic variability. Just LLCF and LPAI showcase positive skewness among the variables; in contrast, LGDP, LGDPSQ, LFGOB, LTI, and LURBA exhibit negative skewness. Furthermore, the Jarque-Bera normality test was applied to ensure that each variable in this investigation had a normal distribution. Since this test accounts for both skewness and any anomalous Kurtosis, it seems logical. Key indicators of statistics including mean, median, maximum, minimum, standard deviation, probability value, total, and sum square are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which offers an exhaustive analysis of the information at hand.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 02\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGDP2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLPAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLFGOB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLTI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLURBA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.835416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.64393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.3917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.0143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.290778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.1409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.377885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.822656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.71885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.8942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.2377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.322086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.27706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.382195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.63269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.15938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.5318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.66873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.385638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.59584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.417309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.970971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.08116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.6297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.55212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.093117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.38458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.32148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.093945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.318778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.76113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.665446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.091946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.408947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.027091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.065531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.255693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.219087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.989543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.146481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.577478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.500595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.965479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.888894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.876795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.466317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.067511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.90974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.252894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque-Bera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.449882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.994763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.938117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.602132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.016314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.363452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.08073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.484353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.368844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.186053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.353326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-26.73331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340.6058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3628.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e704.4575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137.3049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e388.5089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e140.0923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum Sq. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.273596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.150205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1417.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.98507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.262076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.184362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Unit root test\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, all three stationarity tests (ADF, DF-GLS, and P-P) are demonstrated for log-transformed variables at both the level and first difference form. In each of the three unit root evaluations, it appears that only the urbanization factor is stationary at the level I(0), while the load capacity factor, GDP, GDP squared, private investment in AI, financial globalization, and innovations in technology were non-stationary before we considered their first differences. This mixed sequence of integration encourages us to conduct the assessment now using the ARDL methodology.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 03\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Stationarity test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eP-P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDF-GLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI(0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI(0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.347***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.354***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.302***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.841***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.829***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.451***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.968***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.423***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.505***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.403***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.365***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFGOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.140***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.090***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.520***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.053***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.076***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.765***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLURBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.850***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.120***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.781***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 ARDL bound test\u003c/h2\u003e \u003cp\u003eTo find confirmation of co-integration between the variables, the present research employed an ARDL bounds test approach. The null hypothesis that there is no co-integration is rejected at the 1% significance level, based on the ARDL bound test findings. The critical value has been surpassed by the F test statistic result of 5.6945. Therefore, it may be claimed that the parameters of the model have certain co-integrating associations. According to this investigation, the long-term driving forces consist of urbanization, technological innovation, financial globalization, economic expansion, and private investment in artificial intelligence. These characteristics additionally motivate the system to respond first to a typical stochastic disruption. We conclude that the load capacity factor (LCF) in the United States is affected by differences in all of these variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 04\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of ARDL bound test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTest Statistics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eValue\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eF statistics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.6945\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSignificance level\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCritical Bounds\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.50%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eI(0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.99\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.27\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.55\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e2.88\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eI(1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e2.94\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3.28\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e3.61\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e3.99\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4 ARDL Short-run and Long-run\u003c/h2\u003e \u003cp\u003eAfter the cointegration had been verified by the bound testing process, we might evaluate the long-term connection among those variables. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e adopts the dynamic ARDL model to demonstrate the short- and long-term effects of LGDP, LGDP2, LPAI, LFGOB, LTI, and LURBA on LLCF in USA.\u003c/p\u003e \u003cp\u003eThe findings indicate that the load capacity of the US environment seems to decrease with economic expansion over time, but grows with continued expansion of GDP. Urbanization and private investment in AI have been significant contributors to the expansion of load capacity factor, but long-term US LCF is decreased by financial globalization and technological innovation. Our findings demonstrate that the ecosystem gradually loses its natural qualities due to economic development. The US economy is expanding and strongly dependent on energy sources like fossil fuels, which cause ecological damage, therefore the conclusion makes theoretical sense. The results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that the LCF decreases by 3.449% in the long run and by 4.242% in the short run for every 1% increase in GDP. This study's results align with previous studies that established an adverse relationship between GDP growth and load capacity factor. A few studies have concluded that a boost in the GDP has a negative impact on the environment. This includes Xu et al. (2022) for Brazil; Shang et al. (2022) for ASEAN countries; Pata and Balsalobre-Lorente (2022) for Turkey; Khan et al. (2023) in the context of G7 and E7 countries; Akadiri et al. (2022) for India; and Pata (2021) in Japan and the United States. However, for Taiwan, Yeh and Liao (2017) discovered the opposite outcome. They also concluded that Taiwan has come to the point where economic pressures no longer adversely affect the natural world. Likewise, Nathaniel et al. (2020) identified no evidence linking economic expansion to environmental damage in CIVETS (Egypt, Turkey, South Africa, Indonesia, and Vietnam).\u003c/p\u003e \u003cp\u003eOn the other hand, each unit of growth in GDP2 results in a 1.184% long-term and 0.156% short-term improvement in LCF. Given that the coefficient for LGDP is negative and the coefficient for LGDP2 is positive and both are statistically significant this suggests that environmental pressure diminishes over time, supporting the recently proposed LCC hypothesis for the USA. The coefficients for LPAI indicate a positive correlation with LLCF, implying a 0.015% long-term degradation and a 0.462% short-term increase in LCF for every 1% rise in PAI. Thus, private investment in artificial intelligence in the United States significantly contributes to environmental sustainability. According to Karpovich et al. (2022) executing \"green\" investment-innovative projects in intelligent manufacturing operated by artificial intelligence plays an integral part in ensuring the ecological security of Russia's local economy. Moreover, based on Platon (2024), eco-investment and artificial intelligence constitute key elements that might accelerate and improve the circular economy.\u003c/p\u003e \u003cp\u003eConversely, LCF is negatively associated with FGOB in both long and short run, and this relationship is statistically significant. These findings suggest that financial globalization has a adverse impact on the USA ecosystem. Specifically, a 1% increase in FGOB decreases LCF by 1.193% in the long run and by 0.502% in the short run. This result is inconsistent with the research of Akadiri and Adebayo (2021), which shows that, in India, pollution levels grow when financial globalization declines, but they drop when it increases. This inference is supported by findings by Xu et al. (2022) and Akadiri et al. (2022), which found a positive correlation between financial globalization and load capacity factor in Brazil and India, respectively. Generally speaking, the globalization of finance symbolizes the advancement of a country's financial sector; a sophisticated financial system would place investments in ecological sustainability ahead of environmentally damaging growth paths (Raihan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, there is a negative correlation between LTI and LLCF, with each 1% increase in TI reducing LCF by 0.127% in the long run and 0.00253% in the short run, and this result is significant at the 1% level. Research by Su et al. (2021) concluded that advances in technology raised emission levels in Brazil. The above findings illustrate that the United States has not yet invested in or implemented green technologies to ensure environmental sustainability. Similarly, Adebayo and Kirikkaleli (2021) verified that technological developments worsen Japan's environmental conditions and raise carbon emissions. This result aligns with the conclusions of Lin and Zhu (2019). That said, it goes contrary to the studies conducted by Khan et al. (2020) and Shahbaz et al. (2020), which found that technological innovation enhances the environmental condition.\u003c/p\u003e \u003cp\u003eAdditionally, the positive and statistically significant URBA coefficients indicate that both long-term and short-term increases in LURBA negatively affect environmental quality. A 1% increase in URBA raises LCF by 1.182% in the long run and by 17.204% in the short run. These findings suggest that the current urbanization structure in the United States is not conducive to reducing pollution. Research conducted in Singapore by Ali et al. (2017), Saudi Arabia by Raggad (2018), and 19 other countries by Saidi and Mbarek (2017) explored that urbanization improves the sustainability of the environment by lowering emissions of carbon dioxide. But, Wang et al. (2016) found that greater urbanization boosts CO2 emissions. However, the present study\u0026rsquo;s findings contradict Solarin et al. (2021), who reported that urbanization has no harmful impact on the environmental quality of Nigeria.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eARDL Long-Run and Short-Run Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.449***(11.5676)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.184***(0.52519)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015**(0.03079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFGOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.193***(0.2959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.127**(0.16542)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLURBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.182(5.1537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.LGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.242**(4.26804)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.LGDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156**(0.19304)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.LPAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.462***(0.00743)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.LFGOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.502***(0.15540)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.LTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00253(0.0074)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLURBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.204***(2.34298)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECT (Speed Adjustment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.684***(0.08539)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.910***(31.2434)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.8780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Robustness Check\u003c/h2\u003e \u003cp\u003eThe DOLS, FMOLS, and CCR methods are additional techniques employed to assess the validity and reliability of the ARDL outcomes. The findings of this study on robustness are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The results of robustness testing confirm the findings obtained through ARDL calculations.\u003c/p\u003e \u003cp\u003eThe economic growth coefficients in the FMOLS, DOLS, and CCR computations are statistically significant at the 1% level and have negative values. It can be concluded from the estimated components that an increase of 1% in GDP causes the load capacity factor (LCF) to fall by 13.532%, 7.325%, and 14.135%, respectively. The higher R-squared values suggest that the estimation was appropriate. A 1% increase in LPAI leads the LCF to grow by 0.013%, whereas an extra 1% in LGDP2 enables the LCF to expand by 0.625% in the FMOLS model. These figures are noteworthy and corroborate the ARDL results displayed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Additionally, a 1% surge in LURBA raises the LCF by 7.428%, whereas a 1% expansion in LTI encourages the LCF by 0.037%. These outcomes also align with the ARDL short and long run estimation. Conversely, a 1% spike in LFGOB causes the LCF average to drop by 1.243%. Similar to the ARDL results, the coefficients LGDP2, LPAI, and LFGOB are significant at the 1% level of significance, whereas LTI and LURBA are significant at the 5% level.\u003c/p\u003e \u003cp\u003eWithin the DOLS model, an extra 1% in LGDP2, LPAI, LTI, and LURBA raises an average of 0.271%, 0.045%, 0.568%, and 8.671% in LCF. The value of the ARDL results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e05\u003c/span\u003e is confirmed by the statistically significant values of these variables. Conversely, a one percent rise in LFGOB leads to an average 1.930% reduction in LCF. Similar to the ARDL conclusions, the coefficient of LFGOB is significant in this particular case. The CCR observations exhibit a similar pattern, except for the LFGOB example. An average of 0.652%, 0.0153%, 0.045%, and 7.796% of LCF are spiked by an additional 1% in LGDP2, LPAI, LTI, and LURBA in the CCR model. Conversely, an extra 1% in LFGOB causes an average 1.236% decrease in LCF.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Check\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFMOLS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDOLS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCCR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLLCF dependent\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLGDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-13.532***(4.4923)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-7.325**(5.8932)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e-14.136***(6.7150)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLGDP\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.625***(0.2035)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.271**(0.1827)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.652***(0.3053)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLPAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.013***(0.0121)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.045**(0.0985)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.0153**(0.0183)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLFGOB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-1.243***(0.1619)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-1.930***(0.5920)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e-1.236***(0.1923)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLTI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.037**(0.0722)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.568*(0.4841)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.045**(0.0950)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLURBA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e7.428**(2.2595)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e8.671*(3.6591)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e7.796***(2.8327)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e44.608**(18.0927)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e27.8901**(16.5672)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e46.294**(25.9663)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR-squared\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.9013\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.9641\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.9005\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Pairwise Granger Causality test\u003c/h2\u003e \u003cp\u003eThe findings of causal linkages across several economic indicators are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. An F-statistic of 3.38826 and a p-value of 0.0499 indicate that LLGDP does not Granger-cause LLCF. This suggests that we reject the null hypothesis that there is no link between variables at the 5% significance level.\u003c/p\u003e \u003cp\u003eFurthermore, p-values below the usual significance threshold confirmed the observation of one-way causation from LGDP2, LPAI, and LTI to LLCF. Therefore, in these circumstances, we reject the null hypothesis that there is no causation. Nonetheless, a strong two-way causal relationship was discovered between LLCF and LGOB as well as between LURBA and LLCF. On the other hand, p-values higher than the traditional significance level for each case demonstrated that there was no significant causal relationship between LLCF and LPAI, LLCF and LGDP, LLCF and LGDP, or LLCF and LTI. As a result, the null hypothesis that there is no causation in these interactions is not successfully rejected.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 07\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Pairwise Granger Causality test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNull Hypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDP \u0026ne; LLCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.38826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLCF \u0026ne; LGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDP2 \u0026ne; LLCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLCF \u0026ne; LGDP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPAI \u0026ne; LLCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLCF \u0026ne; LPAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFGOB \u0026ne; LLCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.05754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLCF \u0026ne; LFGOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTI \u0026ne; LLCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.76786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLCF \u0026ne; LTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLURBA \u0026ne; LLCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLCF \u0026ne; LURBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.37891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Diagnostic Test\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e08\u003c/span\u003e displays the diagnostic examination outcomes. The results demonstrated that the usefulness of all diagnostic procedures is insignificant, and the null hypothesis cannot be rejected. According to the p-value of 0.8027, the Jarque-Bera test confirms that the residuals appear to be normally distributed. The Lagrange Multiplier analysis shows no serial correlation in the residuals, with a p-value of 0.9463. Lastly, the Breusch-Pagan-Godfrey assessment confirms that the residuals do not exhibit heteroscedasticity, with a p-value of 0.3411.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe findings of diagnostic tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic tests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque-Bera test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResiduals are normally distributed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLagrange Multiplier test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo serial correlation exits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreusch-Pagan-Godfrey test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo heteroscedasticity exists\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present research comprehensively addresses how the LCF in the USA became influenced by private investment in artificial intelligence (AI), economic expansion, financial globalization, technological innovation, and urbanization between 1990 and 2022. The discoveries propose insightful information on the intricate connections between economic activity and the preservation of the environment. To validate the Load Capacity Curve (LCC) theory, the research makes use of advanced econometric techniques. The findings indicate that while urbanization and PAI reduces the environmental burden, technical advancements, economic growth, and financial integration serve to excerbate these consequences. The results of the stationarity tests reveal that the elements in question exhibit a combination of various degrees of integration and do not exhibit unit root problems. The ARDL bound assessment provides further evidence that these factors are cointegrated, indicating the existence of steady long-term linkages. The ARDL calculations demonstrate a favorable association between GDP growth, TI, FGOB, and LCF and provide short- and long-term support for the LCC hypothesis in the USA. This suggests that environmental damage occurs due to economic expansion when insufficient steps are made to safeguard the environment. On the other hand, the positive correlations between GDP, TI, FGOB and LCF convey that these factors might encourage adverse environmental effects. It is anticipated that financial globalization can provide the required funding for investments in eco-friendly technologies and more productive industrial processes. Similar to this, robust and resilient advances in technology when combined with an openness to trade might foster the creation of novel concepts and the use of greener practices by stimulating healthy competition and granting access to the latest technologies. The validity of the ARDL findings is confirmed by the robustness testing employing FMOLS, DOLS, and CCR, which increases the credibility of the results. Furthermore, the Pairwise Granger Causality tests exhibit significant one-way causal relationships between LLCF and LGDP2, LPAI, and LTI. These relationships emphasize the relevance of how economic shifts, private investments in artificial intelligence, and improvements in green technology impact the dynamics of ecological sustainability in the USA. Therefore, this investigation suggests several legislative solutions aimed at encouraging sustainable economic development in the United States by leveraging financial globalization, technical improvements, and a feasible urban infrastructure.\u003c/p\u003e"},{"header":"6. Policy Recommendation","content":"\u003cp\u003eIn order to tackle the U-shaped correlation discovered in our study between income and environmental sustainability, the United States should adopt a comprehensive and diverse policy strategy. At first, the focus should be on providing green technology and sustainable practices to lower-income areas. This may be done by offering subsidies for the adoption of renewable energy and providing incentives for eco-friendly enterprises. As income levels increase, it is necessary to enhance laws in order to reduce environmental degradation caused by higher levels of consumption and industrial operations. This entails implementing rigorous emissions regulations, advocating for energy conservation, and allocating resources towards sustainable infrastructure. To promote sustainability among high-income groups, policymakers can incentivize investments in clean energy through tax benefits, implement carbon pricing systems, and allocate funds for new environmental technology. Furthermore, it is imperative to strengthen education and awareness initiatives on sustainable behaviors among individuals of all income brackets in order to cultivate a societal ethos of environmental accountability. It is imperative for federal and state governments to cooperate in order to guarantee the efficient implementation of these policies, while also customizing them to suit the specific requirements of each region. Through the implementation of this all-encompassing strategy, the United States may utilize economic expansion to enhance environmental results and attain enduring sustainability.\u003c/p\u003e \u003cp\u003eIn order to maximize the beneficial effects of private investment in AI on environmental sustainability, the United States should implement specific and focused regulatory initiatives. Firstly, offer tax incentives and subsidies to private firms that invest in AI technologies that improve environmental sustainability, such as smart grids, precision agriculture, and predictive maintenance to minimize waste and emissions. Facilitate the formation of collaborations between the public and commercial sectors to expedite the implementation of sustainable solutions powered by artificial intelligence. This will ensure that even small and medium-sized firms have the opportunity to benefit from these advancements. Enforce policies that promote openness and accountability in the use of AI technology to mitigate unanticipated adverse environmental effects. Increase research and development funding for artificial intelligence (AI) programs that specifically target sustainability, with an emphasis on promoting innovation in areas such as climate modeling, resource management, and energy efficiency. Furthermore, advocate for the use of artificial intelligence (AI) into environmental monitoring and enforcement endeavors to enhance adherence and effectiveness. Advocate for workforce development projects that focus on cultivating proficiency in artificial intelligence and environmental sustainability. This will ensure the availability of a highly qualified labor force capable of driving progress in these areas. To leverage technology developments and establish itself as a frontrunner in the green economy, the United States may provide a favorable climate for private investment in AI, therefore promoting significant strides in environmental sustainability.\u003c/p\u003e \u003cp\u003eIn order to counteract the negative effects of technical innovation and financial globalization on reducing the load capacity factor, the United States should implement a strategic policy framework. Firstly, establish policies that promote the use of sustainable technical innovations, with a focus on optimizing resource utilization and reducing environmental impacts. Offer incentives to encourage enterprises to create and use environmentally friendly technologies that increase the ability to handle workloads without using up resources. Facilitate responsible financial globalization by implementing regulations that guarantee investments uphold sustainable practices and refrain from exploiting natural or human resources. Enhance global collaboration to harmonize worldwide financial transactions with sustainability objectives, guaranteeing that overseas investments and technology transfers make a positive contribution to environmental sustainability. Promote and fund research and development in sustainable technologies and practices, encouraging innovative solutions that achieve a balance between economic growth and environmental stewardship. In addition, improve education and training programs that specifically target sustainable practices and the environmental consequences of globalization, equipping the workforce to actively participate in and promote these endeavors. Through the incorporation of these policies, the United States can effectively tackle the difficulties presented by technical advancement and financial globalization, guaranteeing long-term growth and safeguarding the nation's ability to support future generations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad S, Raihan A, Ridwan M (2024) Role of economy, technology, and renewable energy toward carbon neutrality in China. Journal of Economy and Technology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury AAA, Rafi AH, Sultana A, Noman AA (2024) Enhancing green economy with artificial intelligence: Role of energy use and FDI in the United States. arXiv preprint arXiv:2501.14747\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury AAA, Sultana A, Rafi AH, Tariq M (2024) AI-driven predictive analytics in orthopedic surgery outcomes. 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Jinnah Business Review, 11(1)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Obafemi Awolowo University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Financial Globalization, LCC Hypothesis, Private Investment in AI, Technological Innovation, United States","lastPublishedDoi":"10.21203/rs.3.rs-5953542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5953542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the role of private investment in Artificial Intelligence (AI) in promoting environmental sustainability in the United States from 1990 to 2019. It also analyzes the impact of financial globalization, technological innovation, and urbanization by testing the Load Capacity Curve (LCC) hypothesis. The study employs stationarity tests, which indicate that the variables are free from unit root problems and exhibit mixed orders of integration. Using the Autoregressive Distributive Lag (ARDL) Model bound test, the study finds that the variables are cointegrated in the long run. The short-run and long-run estimations of the ARDL model confirm the existence of the LCC hypothesis in the United States, revealing a U-shaped relationship between income and load capacity factor. The results show that private investment in AI has a significant positive correlation with the load capacity factor, thus promoting environmental sustainability. Conversely, technological innovation and financial globalization exhibit a negative correlation with the load capacity factor in both the short and long run. To validate the ARDL estimation approach, the study employs Fully Modified OLS, Dynamic OLS, and Canonical Correlation Regression estimation methods, all of which support the ARDL results. Additionally, the Granger Causality test reveals a unidirectional causal relationship from private investment in AI, financial globalization, economic growth, technological innovation, and urbanization to the load capacity factor.\u003c/p\u003e","manuscriptTitle":"Analyzing How AI impact Environmental Sustainability: Case Study for USA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-05 05:27:04","doi":"10.21203/rs.3.rs-5953542/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"604d3dbc-01ca-4adb-9ea9-9f29326d4737","owner":[],"postedDate":"February 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-05T05:27:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-05 05:27:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5953542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5953542","identity":"rs-5953542","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}