Unidirectional Causality Research Articles

Financing tech-driven industrialization in Africa: Resource wealth, financial structure and the rise of medium- and high-tech manufacturing

This study examines the roles of natural resource wealth and financial structure in driving industrial diversification into medium- and high-tech manufacturing (TM) via a panel dataset consisting of 24 African countries from 1991 to 2021. By utilizing the method of moments approach to quantile regression and the HPJ Wald-type Granger non-causality test, this study provides insights pertinent to achieving sustainable development goal 9.B. The results reveal a U-shaped relationship between per capita GDP and TM, where early stages of economic growth hinder industrial diversification, but higher growth levels stimulate tech-driven industrialization. The study confirms that combined income from energy, minerals, and forest wealth negatively affects TM, particularly in less industrially diversified economies, thus supporting the resource curse hypothesis. Financial structure plays a crucial role; market-based systems foster TM, whereas institution-led financial development tends to impede it. The Granger non-causality tests reveal unidirectional causality from natural resource wealth, institution-led financial development, and market-based financial development to TM, with bidirectional causality between per capita GDP and TM. To fully leverage these findings, African economies should prioritize the development of robust financial markets, implement comprehensive banking reforms, and strategically invest resource revenues in technology-driven sectors.

Do geopolitical uncertainty and inflation transmit opportunities or threats to green energy consumption?

Purpose This study aims to examine the impact of geopolitical uncertainty and inflation on green energy consumption. In addition, it aims to reveal the cause-and-effect relationship among these variables. Design/methodology/approach The authors exert the panel dynamic ordinary least squares (OLS) approach of Kao and Chiang (2000) and the Granger non-causality method of Dumitrescu and Hurlin (2011). On the other hand, current study relies on the annual time series data of 29 countries from 1985 to 2022. Findings The panel dynamic OLS results confirm a long-run relationship between geopolitical uncertainty, inflation and green energy consumption. On the other hand, inflation negatively impacts green energy consumption, with high inflation levels potentially halting the transition. Conversely, geopolitical uncertainty shows no significant effect on green energy use, indicating a reliance on traditional energy sources. Moreover, the current investigation reveals the unidirectional causality from green energy consumption to inflation, while no short-run causality exists from inflation as well as geopolitical uncertainty to green energy consumption in the selected countries. Research limitations/implications The authors drew conclusions about the potential impact of geopolitical uncertainty and inflation on green energy uses by considering the macro-level data. However, this investigation further be enhanced by looking at such issues through the micro perspective by understating the thought and perspective of public on the implication of geopolitical uncertainty and inflation on green energy uses. Such micro-level, country-specific study would definitely help us to generate more concrete ideas about such themes. Practical implications The empirical findings have important implications for policymakers. Results suggest that high inflation negatively impacts green energy use, so policymakers must consider implementing measures to control the inflation while promoting green energy uses for economic prosperity and environmental sustainability. In the short run, green energy use leads to inflation. Therefore, macroeconomic policymakers can implement various subsidy policies for the general public to mitigate the short-term inflationary impact of green energy use. Originality/value This investigation is novel in three ways. First, it explores the impact of geopolitical uncertainty and inflation on green energy use, using Caldara and Iacoviello’s (2022) geopolitical risk index as a proxy of geopolitical uncertainty. Second, it uses panel data econometric analysis, a method that most previous studies have not contemplated for this type of investigation. Finally, by including both advanced and emerging economies, it provides valuable insights for policymakers to develop effective strategies related to green energy consumption for achieving sustainable economic development.

Human Resource Indicators and Bank Performance

Accelerated globalization and the thirst for economic emancipation of the people create demographic shifts from developing countries to developed countries. To assist policymakers in effective decision making regarding labor migration and economic growth, our paper explores the hidden dynamics between labor migration and economic growth in Bangladesh from 1988 to 2020. The study was developed based on the Auto Regressive Distributed Lag (ARDL) framework to capture long-run and short-run dynamics among the variables. To find the directional relationship, we also perform the Toda Yamamoto Causality test to find the short-run impact on the dependent variables. The existence of co-integration among the variables is demonstrated by the Bounds test result. The empirical results show the long and short-run positive significant relationship between international labor migration and exchange rate. However, Inflation and GDP growth do not have any significant impact on migrant laborers from Bangladesh to abroad. The causality approach indicates a unidirectional causality between labor migration and exchange rate, GDP growth, and exchange rate. This paper suggests that the devaluation of the local currency would be a possible way to boost labor in Bangladesh.

THE IMPACT OF EMPLOYMENT IN SELECTED SECTORS ON ECONOMIC ROWTH: THE CASE OF IRAQ

The aim of the research is to analyses the effect of employment on economic growth and to assess the impact of this effect on agriculture, industry and services. In line with the findings from the related literature, this study applied an ARDL bounds test and Toda Yamamoto causality analysis on the Iraqi case. The study found that, employment increases in agriculture, industry and services sectors have a positive impact on economic growth. Long-run coefficient results reveal that increases in employment in these sectors has a positive relationship with economic growth. In particular, increases in employment in the agricultural sector are found to significantly support economic growth. In accordance with the results of the Toda-Yamamoto causality analysis, there is a unidirectional causality relationship between economic growth and agriculture, industry and services sectors. This can be explained by the interaction of various sectors of the economy with each other and this interaction encourages economic growth. Although agriculture, industry and services sectors have different contributions to economic growth, each of them has a positive effect on growth.

Challenges for low-carbon economies in Latin America. Testing pollution haven hypothesis in developing countries

Our research investigates the determinants of CO₂ emissions in Latin American countries (LAC), focusing on the roles of renewable energy consumption (RENC), economic growth (GDP), foreign direct investment (FDI), urbanization (URB), and access to electricity (ACEL). Employing a panel ARDL model, we account for both short-run and long-run dynamics between CO₂ emissions and their determinants. In the long run, RENC demonstrates a significant negative impact on CO₂ emissions (-0.15). Higher-order terms of RENC reveal non-linear effects, with RENC² showing a positive coefficient (0.05) and RENC³ a negative coefficient (-0.006), indicating diminishing returns as renewable energy consumption increases. Economic growth is positively associated with CO₂ emissions (0.1), reflecting the trade-off between economic expansion and environmental sustainability. FDI has a small but significant negative impact on emissions (-0.0003), while ACEL also reduces emissions (-0.03). In the short run, CO₂ emissions adjust to long-run equilibrium at a rate of -0.04, with GDP exerting the strongest short-run influence. The Energy Kuznets Hypothesis for CO₂ emissions is found to be a monotonically decreasing function of RENC, implying that as RENC increases, CO₂ emissions consistently decrease, albeit at a slower rate. The error correction term (ECT) is negative and significant (-0.04), indicating that CO₂ emissions adjust towards the long-run equilibrium by about 4% each period. The Dumitrescu-Hurlin panel causality test confirms bidirectional causality between RENC and CO₂ emissions. Unidirectional causality is also observed from URB to CO₂ emissions and from CO₂ to ACEL. Variations in energy consumption profiles and stages of economic development across the region explain differing impacts of RENC and FDI on emissions, where some countries experience the pollution haven effect, while others benefit from the pollution halo.

Energy Price Impact on BRIC Stock Markets: A Granger Causality Analysis

Energy prices and the stock market are two of the crucial factors in the evolving landscape of global finance, particularly in major emerging economies. However, research on how energy price changes impact stock markets in BRIC countries remains limited, despite their diverse roles in global energy markets and economies. This study investigates the causal dynamics between energy prices and stock market performance in BRIC countries, aiming to uncover short-term fluctuations and long-run relationships in these major emerging economies. Utilizing daily data from 2013 to 2023, stationarity tests, cointegration analysis, and Granger causality tests are employed to examine these relationships. Key findings reveal weak evidence of a long-run equilibrium between energy prices and stock market indices, challenging previous assumptions about their cointegration. More significantly, the findings uncovered a strong unidirectional Granger causality from oil prices to all BRIC stock market indices, while gas prices show a more selective influence. Notably, no evidence of reverse causality from stock markets to energy prices was found, highlighting the exogenous nature of global energy prices in relation to BRIC stock markets. This study uniquely analyzes oil and gas price effects on BRIC stock markets, offering insights for investors and policymakers amid increasing commodity-financial market integration. Doi: 10.28991/ESJ-2024-08-06-015 Full Text: PDF

The impacts of different types of globalization on energy security risk: Can globalization be a remedy for the energy security risk of OECD countries?

This study analyzes the impact of globalization and its sub-dimensions on energy security risk for OECD countries. In our study, annual data of 26 OECD countries for the period 1980-2018 were used. Second generation panel data techniques were used as the methodology. The results of the analysis show that economic, political and aggregate globalization increases energy security risk, while social globalization is statistically insignificant. The Dumitrescu-Hurlin panel causality test results demonstrate that there is a unidirectional causality relationship between inflation and energy security risk, and a bidirectional causality relationship between economic globalization, social globalization, political globalization, aggregate globalization, GDP and Gross Fixed Capital Formation and energy security risk. The study also analyzed how the impact of globalization on energy security risk varies across countries. The results of the analysis reveals that there are significant differences in the direction, significance and coefficient size of the relationship across countries. These findings suggest that the effects of globalization on energy security risks vary across countries. Therefore, countries should develop policies to minimize energy security risks by taking into account their own dynamics.

Effects of Social and Economic Development on CO2 Emissions in the Countries of the Visegrad Group

The aim of this study is to assess the impact of socio-economic development, consumption of renewable energy, educational spending, and foreign direct investment on carbon dioxide emissions in the Visegrad countries (Czech Republic, Hungary, Poland, and Slovakia) between 1991 and 2021. The study employed the Autoregressive-Distributed Lag Bound Testing model and Toda–Yamamoto causality tests to establish short- and long-term relationships for each of the dependent variables and countries, aiming to verify the hypotheses. For the Czech Republic and Slovakia, a bidirectional relationship was identified between carbon emissions and the Human Development Index (HDI). In Poland, bidirectional causality was indicated between renewable energy consumption and carbon dioxide emissions. Unidirectional causality was evident for the Czech Republic, Hungary, and Slovakia between the specified variables. Short-term findings indicate that an increase in renewable energy production may lead to higher carbon dioxide emissions in the Visegrad countries. In the long term, only Slovakia demonstrates a significant relationship between educational spending and reduced carbon emissions. Toda–Yamamoto tests reveal unidirectional causality between foreign direct investment and carbon dioxide emissions for the Czech Republic and Slovakia. Empirical findings suggest that policymakers should invest in socio-economic development and renewable energy to reduce CO2 emissions and achieve long-term sustainability in the Visegrad countries. Poland needs to shift its energy policy away from coal, and all countries should enhance education to effectively transmit environmental knowledge and values.

Genetically predicted blood metabolites mediate the association between circulating immune cells and severe COVID-19: A Mendelian randomization study.

Investigating the causal relationship between circulating immune cells, blood metabolites, and severe COVID-19 and revealing the role of blood metabolite-mediated circulating immune cells in disease onset and progression. Genetic variation data of 731 circulating immune cells, 1400 blood metabolites, and severe COVID-19 from genome-wide association study open-access database (https://gwas.mrcieu.ac.uk) were used as instrumental variables for bidirectional and two-step Mendelian randomization analysis. The study identified 11 circulating immune cells with unidirectional causality to severe COVID-19. Two-step Mendelian randomization analysis showed 10 blood metabolites were causally associated with severe COVID-19, and blood Myristate and Citrulline to phosphate ratio mediated the association of circulating effector memory double negative % DN and CD8dim natural killer T cell % T cells, respectively, with severe COVID-19 (Myristate mediated effect ratio was 10.20%, P = .011; Citrulline to phosphate ratio mediated effect ratio was -9.21%, P = .017). This study provides genetic evidence assessing the causal relationship between circulating immune cells, blood metabolites, and severe COVID-19, elucidates the role of blood metabolite-mediated circulating immune cells in severe COVID-19 development, and offers new insights into severe COVID-19 etiology and related preventive and targeted therapeutic strategies.

A VAR Framework of Exchange Rates, Interest Rates, and Inflation Through COVID-19 in Turkey: Empirical Evidence From Linear Cointegration and Causality Analysis

In this study, we investigate the impact of exchange rates and interest rates on inflation in Turkey using monthly data from January 2004 to July 2020 obtained from the Turkish Statistical Institute. Our vector autoregressive (VAR) model showed an evidence of stochastic behaviour among the series. The autoregressive distributed lag (ARDL) results showed a short-run and long-run covariate between exchange rates, interest rates and inflation. Specifically, the findings showed a short-run and long-run relationship between inflation, producer price index (PPI), and Turkish interbank offer rates (TIBOR). However, there was no connection between inflation and dollar exchange rates (DSR) and commercial banks’ interest rates (CBIR). The VAR Granger causality results revealed the variables to be exogenous except DSR, which displayed endogenous to other variables. Nevertheless, the results revealed unidirectional causality from the producer price index (PPI) to DSR and unidirectional causation from TIBOR to CBIR. It means an increase in production costs through raw materials importation led to devaluation of the Turkish lira. Similarly, TIBOR rates drive CBIR high, making domestic lending more expensive, which will inhibit loan provisions to the private sectors, which will result in an economic contraction and eventually high inflation. Our unit roots breakpoint results pointed to breaks in the dataset were between 2016-2019, reflecting the effects of the Fetullah Terrorist Organization (FETO) failed coup, the 2018 U.S. embargo, and an assumed fiscal dominance as the major and direct causes of economic instability and inflation. However, Covid-19 may have acted as a contributing factor since then. Thus, we recommend that the monetary authorities articulate policy to avoid the assumed fiscal dominance.

Investigation of the relationship between economic growth and mismanaged construction waste: Evidence from 268 cities in China

Developing countries face the unprecedented environmental protection challenges associated with urban regeneration and economic development. These challenges are particularly evident in managing the vast quantities of construction waste (CW) generated by new building construction and demolition at an urban scale. Capturing the dynamic relationships between CW and urban economic development is a key step in addressing the imbalance between the two and in exploring effective management strategies. This study innovatively uses urban-level data to analyze the non-linear relationship between CW and economic development, while verifying the applicability of the Environmental Kuznets Curve (EKC) hypothesis in the context of CW management. By exploring both short-term and long-term equilibrium effects using data from selected Chinese cities, the study investigates the heterogeneity among four agglomerations through hybrid panel econometric methods. Subsequently, quantitative data analysis is conducted to uncover the socioeconomic drivers of CW generation and to explore their unidirectional causality relationships. This approach not only provides crucial evidence for developing a strategic framework for CW management, but also extends the applicability of the EKC hypothesis to the domain of CW.

Navigating Carbon Peaking and Neutrality in MENA: The Impact of Foreign Direct Investment and Trade Openness Using Panel PCSE and FGLS Techniques

ABSTRACTIn response to the global challenge of climate change and the drive towards carbon neutrality, this study investigates the effects of foreign direct investment (FDI) and trade openness (TO) on carbon emissions (CEM) within the Middle East and North Africa (MENA) region. The research spans 18 countries from 1990 to 2022, focusing on Sustainable Development Goal 13. Utilizing Porter's hypothesis and the Environmental Kuznets Curve (EKC) theory, the study applies quantitative methods including panel corrected standard errors (PCSE) and feasible generalized least squares (FGLS) to ensure robustness. To address potential endogeneity, two‐stage least squares and lagged effect estimations are employed. The findings reveal that both FDI and TO are associated with reduced CEM, indicating positive environmental impacts. Also, the pairwise causality tests show bidirectional causality between FDI, TO, and CEM, while a unidirectional causality is found from industrialization (IND) and energy consumption (EC) to CEM. These results underscore the importance of integrating FDI and TO into strategies for achieving carbon neutrality and advancing sustainable development goals, offering actionable insights for policymakers.

Foreign Portfolio Investment and Capital Market Development in Nigeria

This study investigates the impact of foreign portfolio investment (FPI) on the development of the Nigerian capital market from 1986 to 2023, addressing challenges such as exchange rate volatility and inadequate technological advancement. The research aims to explore the following questions: What is the relationship between foreign portfolio investments and capital market development in Nigeria? How do exchange rate volatility influence the capital market development in Nigeria? What role do technological advancements play in enhancing market performance and attracting foreign investors? Data were sourced from the Central Bank of Nigeria Statistical Bulletin and the National Bureau of Statistics. A quantitative methodology was employed, utilizing the Johansen co-integration test, Vector Error Correction Model (VECM), and Granger causality test to analyze secondary time-series data. The Johansen co-integration test revealed a long-run relationship among the variables, while the VECM indicated that FPI negatively impacts market capitalization. Additionally, the Granger causality test demonstrated unidirectional causality from capital market development to FPI. To address the identified challenges, the study recommends that policymakers create a more supportive environment that ensures foreign investors can repatriate capital and earnings. The government should prioritize exchange rate stability that reflects market realities to enhance investor confidence. Investments in advanced technology are crucial to improving efficiency and accountability in market transactions. By adopting these targeted recommendations, Nigeria can better leverage FPI to foster capital market growth and promote sustainable economic development. JEL Codes: G15, O16

Modern methodological approaches for evaluating energy efficiency and environmental pollution: Emerging trends and technological innovation perspectives

This study analyzes the causal link between technological advancement, environmental degradation, energy usage, and sustainable economic development in China. The paper looks at the extent to which expansion in energy and carbon dioxide emissions can be explained using the Environmental Kuznets Curve model. Only the annual data from the WDI is considered; therefore, only the data range from 1990 to 2019 is used. This work also uses the FMOLS technique to establish the nature of the long-run causal relationship between the variables used in the analysis and establish that environmental quality has worsened in China during the period considered. The result shows that renewable and non-renewable energy sources, along with technological advancement, have a significantly positive impact on CO2 emissions, suggesting that they are detrimental to the environment. Nevertheless, the results confirm the EKC hypothesis since the coefficient estimates for GDP growth are positive, whereas those for the square of this variable are negative. The results also highlight the existence of a unidirectional causality between the Chinese GDP and carbon emissions while reporting on a bi-directional causality between economic growth and energy consumption. This research explores the impact of technological changes and energy consumption on environmental pollution in China, examining the issue left unexplored in understanding the effects of technological change and energy usage on sustainable economic growth. Although many prior studies have focused on the EKC model, very little research has specifically explored the effects of both renewable and non-renewable energy sources on China's environmental quality. This paper is, therefore, a direct attempt at filling this gap by identifying the causal effect of these factors over the 1990–2019 period.

Toward Environmental Sustainability: The Nexus between Agriculture, Renewable Energy, and Economic Growth in Mitigating CO2 Emissions in Somalia

This research investigates the intricate relationships between agriculture, renewable energy, economic growth, and carbon dioxide (CO2) emissions in Somalia from 1991 to 2022. This study uses the Autoregressive Distributed Lag (ARDL) model and Granger causality tests to understand their impact on environmental sustainability. ARDL results reveal a negative role of agriculture and renewable energy on CO2 in the short and long term. The country's reliance on fossil fuels and biomass further intensifies CO2 emissions. While economic growth is essential for improving living standards, it positively correlates with CO2 emissions in the short and long term, emphasizing the challenge of decoupling economic development from environmental degradation. Domestic investment has a short-run relation only to CO2 emissions. In Granger causality tests, the results indicated that Agriculture and domestic investment have bidirectional causality to carbon dioxide emissions. On the other hand, renewable energy, economic growth, and domestic investment have unidirectional causality to Agriculture in Somalia, while domestic investment has bidirectional causality to renewable energy and economic growth. The study recommends implementing comprehensive and integrated approaches to prioritize sustainable development strategies, clean energy alternatives, and efficient agricultural practices. Promoting economic growth while also focusing on capacity-building and awareness campaigns is essential. Collaborating with the international community on climate change mitigation and sustainable development initiatives can further support Somalia's journey toward environmental sustainability.

Fostering a Sustainable Future in Somalia: Examining the Effects of Industrialization, Energy Consumption, and Urbanization on Environmental Sustainability

While existing research has extensively explored the individual impacts of energy consumption, industrialization, and urbanization on environmental sustainability, there remains a notable gap in understanding the synergistic effects of these factors within the context of rapidly developing regions, such as Somalia. Therefore, this study explores the impacts of energy consumption, industrialization, and urbanization on environmental sustainability in Somalia from 1990 to 2020. The long-run linkage among the variables of the study was confirmed using the bounds-testing approach. The findings from the ARDL model indicate that economic growth, energy consumption, and trade openness significantly exacerbate environmental pollution in Somalia in the short- and long-run. Conversely, while industrialization and urbanization reduce environmental pollution both the short- and long-run, urbanization does not demonstrate a statistically significant impact. The long-run findings of the study were validated using the DOLS and FMOLS approaches. The Granger causality analysis reveals unidirectional causalities from environmental pollution to GDP per capita, industrialization, and trade openness, as well as from energy consumption to environmental degradation. However, no causal linkage is detected between urbanization and environmental pollution within Somalia. Based on the results of the study, we propose the promotion of renewable energy sources, green industrialization strategies, urban planning and sustainable development, and enhanced monitoring and regulation to effectively address environmental degradation.

Exploring the drivers of ecological footprint: Impacts of road transportation infrastructure, transport tax, and environment technologies

This study explores the dynamic effects of road transportation infrastructure, transport taxes, economic growth, trade openness, and environmental technologies on the ecological footprint using three quantile regression techniques: Simultaneous, Powell, and Method of Moments Quantile Regression (MMQR). These variables are selected for their significant influence on both economic outcomes and environmental sustainability, reflecting the interconnectedness of transportation systems, government policies, and technological advancements in shaping ecological impacts. Based on panel data from 19 European countries between 1995 and 2020, the results reveal that investments in road infrastructure and higher transport taxes contribute to reducing ecological footprint. At the middle and high quantiles, advancements in environment-related technologies further decrease the ecological footprint, whereas trade openness tends to worsen ecological quality. Importantly, the relationship between GDP and the ecological footprint follows an inverted U-shape, confirming the Environmental Kuznets Curve (EKC) hypothesis across all quantiles, which suggests that economic growth can support environmental sustainability in the long run. These findings are validated by robustness checks using fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegration regression (CCR) models. Key policy implications include the need for targeted investments in road infrastructure and transport tax strategies, tailored to national contexts, along with technology-driven strategies to curb environmental degradation. Lastly, the panel causality test reveals unidirectional causality from economic growth, road transportation infrastructure, and environment-related technologies to ecological footprint, highlighting their significance as short-term contributors to environmental improvement.

The Impact of Capital Expenditure on Ethiopian Economic Growth

This study investigates the effect of disaggregated capital expenditure on economic growth in Ethiopia over the period from 1981 to 2021. An autoregressive distributed lag (ARDL) model, combined with a Granger causality test, is used for the econometric analysis. The empirical results reveal that capital expenditure on economic development (CAEE), recurrent expenditure (RCE), and the inflation rate (CPI) have significant positive impacts on economic growth in both the long run and short run. However, capital expenditure on administrative and general development (CEAG) has a significant negative effect on economic growth in the long run. The Granger causality test further indicates unidirectional causality from economic growth (GNI) to capital expenditure on economic development (CAEE). Based on these findings, the study recommends that the Ministry of Finance should increase its budget allocations for capital expenditures aimed at economic development in order to further stimulate economic growth. Additionally, the government should implement stricter follow-up and monitoring mechanisms to ensure the proper management of budget allocations, particularly regarding capital expenditures on social development and administrative and general development. These measures are crucial for sustaining long-term economic growth in Ethiopia.

Impact of Energy Consumption from Renewable Energy Sources on Economic Growth: Evidence from Nigeria

This study examines the impact of renewable energy consumption and electricity tariff on the economy of Nigeria. The study considers the casual relationship and vector decomposition between various renewable energy sources (solar, hydro and biomass), electricity price and Gross domestic product (GDP) using an unrestricted vector error correction model (VECM). In addition, other robust econometric techniques were applied to the time series of GDP, electricity price and energy consumption from renewable energy sources from 1980 to 2021. The findings indicated a 1% increase in biomass consumption causes increase in GDP by 0.14% in the long-run. Mixed result in the short-run with the difference in the log value of the current lag of solar and bio electricity consumption having positive impact on GDP. The coefficient of the Error Correction Model (ECM) was negative (-0.49) and statistically significant indicating that short-run change from the long-run equilibrium is corrected by 49% annually. Unidirectional causality from GDP to solar electricity consumption. Solar, hydro, biomass and electricity price explain 1.4%, 0.4% 2.2% & 12% respectively of fluctuations in GDP in the long-run. The study results demonstrates that regulations need to be put in place to control the adverse effect of consuming biomass on the environment which could cause mixed impact on gross domestic product in the short run whereas, policies to foster development of solar projects could impact positively on GDP and alleviate the electricity supply deficiency in Nigeria.

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

This study investigates the impact of AI innovation on environmental sustainability in the G-7 region from 2010 to 2022. Additionally, it tests the Load Capacity Curve (LCC) hypothesis in relation to financial accessibility, globalization, and urbanization. Cross-sectional dependence and slope homogeneity tests reveal the presence of cross-sectional dependence and heterogeneity issues. Panel unit root and panel cointegration tests confirm that the variables are free from unit root problems and are cointegrated in the long run. To identify significant factors influencing environmental sustainability, this study employs Panel ARDL and Quantile Regression methods. Both methods confirm the LCC hypothesis in the G-7 region, demonstrating a U-shaped relationship between income and the load capacity factor. The results indicate that AI innovation and financial accessibility are significantly positively correlated with the load capacity factor, while globalization and urbanization are negatively correlated, leading to lower environmental sustainability. To validate the robustness of the Panel ARDL and Quantile Regression results, Driscoll-Kraay standard errors, Augmented Mean Group, and Common Correlated Effects Mean Group estimation approaches are applied, all of which support the initial findings. Furthermore, the D-H causality test reveals unidirectional causality from economic growth, financial accessibility, globalization, and urbanization to the load capacity factor, and bidirectional causality between AI innovation and the load capacity factor.