This study investigates the Load Capacity Curve hypothesis to analyze the relationship between GDP per capita and the Load Capacity Factor using panel data from 147 countries over the period 1995–2018. The study employs the classifier-Lasso method, developed by Su, Shi, and Phillips (2016) to address slope heterogeneity. This approach groups countries with similar economic and environmental characteristics, allowing for the estimation of group-specific coefficients. The findings reveal that developed countries exhibit a U-shaped relationship between GDP per capita and environmental outcomes. In the early stages of economic growth, environmental degradation occurs due to industrialization and resource-intensive activities. However, once a certain GDP per capita threshold is surpassed, environmental quality improves as these nations adopt cleaner technologies and implement stricter environmental regulations. In contrast, developing countries display an inverted U-shaped relationship. As GDP per capita increases beyond a specific turning point, industrial expansion and inadequate environmental management lead to significant environmental degradation. These results highlight the complex relationship between economic growth and environmental sustainability, offering valuable insights for policymakers. For developed countries, the study emphasizes the need to uphold stringent environmental standards, foster technological innovation, and take the lead in global sustainability initiatives. For developing countries, it recommends building adaptive capacity, integrating sustainability into economic development strategies, and securing international support to implement effective environmental policies.

Exploring the influencing factors of the ecological footprint is a current focus. However, the impacts of globalization on the ecological footprint are inconclusive due to the complexity among variables. The objective of this paper is to investigate the effects of globalization on the ecological footprint in 35 European countries in 1970–2020. While conducting the research, causality, which goes beyond correlation, was emphasized, and the new panel noncausality test is employed. The results indicate that globalization is generally Granger-cause of the ecological footprint in European countries. Additionally, the individual causation test results also show a unidirectional causality from economic globalization to the ecological footprint, bidirectional causality between social globalization and the ecological footprint, and no causality exists between political globalization and the ecological footprint. In conclusion, this paper not only provides valuable insights into the complex dynamics between globalization and ecological footprints but also offers nuanced policy recommendations tailored to European countries. These recommendations are designed to guide them toward achieving long-term sustainability in the face of the intricate relationships between globalization and ecological footprints.

This comprehensive study addresses the urgent global challenges of climate change and environmental degradation by focusing on the Ecological Footprint (EF). Unlike previous studies, it introduces a novel approach incorporating spatial spillover, temporal effects, and common shocks in panel data analysis. The spatial spillover effect highlights the influence of trade, pollution havens, and competition between neighboring countries on EF. The temporal effects emphasize the significance of historical production patterns and export strategies in shaping the current EF. The study also considers the impact of exogenous common shocks, such as international agreements and global events, on EF. Utilizing a dynamic spatial panel data model with common shocks, the research examines 40 European countries from 1992 to 2020, revealing the significant impact of biocapacity, energy consumption, industrialization, and globalization on EF. Findings indicate that spatial spillover effects contribute to EF transfer, emphasizing the need for collaborative global efforts. The study sheds light on the interconnectedness of environmental impacts and underscores the importance of considering both weak and strong forms of cross-sectional dependence in achieving accurate estimations. The research enriches our understanding of EF determinants and provides nuanced insights for policymakers striving to develop effective strategies for sustainable resource management and environmental conservation.

This study analyzes inflation dynamics in Azerbaijan, focusing on the impact of oil price fluctuations and macroeconomic variables using advanced econometric methods, specifically a time-varying coefficient vector autoregressive model and time-varying Granger causality analysis. The research identifies significant inflation inertia, where past inflation rates shape future expectations. The relationship between oil prices and inflation has evolved, with contractionary policies mitigating the inflationary effects of rising oil prices after 2011, but oil price volatility and external inflationary pressures resurfacing post-2021. Azerbaijan's exchange rate policy reduced inflation by lowering import prices before 2017, but its effectiveness has since weakened. Fiscal policy, particularly changes in M2, has played a key role in inflation dynamics, with contractionary measures after 2015 reducing inflation, while expansionary policies in 2022 reignited inflation. The study also shows that growth in the non-oil sector helps alleviate inflation, though its influence has diminished over time. The study concludes that effective policy measures are essential to manage inflation inertia, reduce dependence on oil revenues, and strengthen economic resilience in a rapidly changing global landscape.

This study examines the effect of artificial intelligence on unemployment in high-tech developed countries. While artificial intelligence is more discussed in futuristic aspects, comprehensive empirical studies are limited in the literature. Therefore, this study uses the dataset, which includes 24 high-tech developed countries from 2005 to 2021 and examines the relationship between a country's Google Trend Index related to AI and the unemployment rate. In the empirical approach, we control the dynamic effect of unemployment by using dynamic panel data and GMM-system estimation to determine the effect of AI on unemployment. The main results show that artificial intelligence decreases the level of unemployment, and the ‘displacement effect’ of AI is validated.

This study aims to investigate the impact of renewable energy consumption on the economic growth of G7 countries and explore the potential nonlinear relationship between the two variables. Initially, the NARDL model is employed to analyze the G7 countries, allowing for the control of nonlinear relationships and considering asymmetric effects. The findings of the NARDL model reveal an asymmetric long run cointegration relationship between renewable energy consumption and economic growth in Canada and the US, while other countries show different dynamics. Subsequently, a causal dynamic impact analysis is conducted to gain further insights into the relationship between renewable energy consumption and economic growth. In the next step, the advantage of panel data analysis is utilized to investigate the overall impact across all G7 countries. For this purpose, the study extends the NARDL model to the PNARDL (Panel Nonlinear Autoregressive Distributed Lag) model, which facilitates the control of asymmetric effects and nonlinearity in the panel data model. In this context, this study is one of the first studies to control the nonlinearity in panel data analysis. The results from the PNARDL model demonstrate that renewable energy has a positive long-term relationship with economic growth in G7 countries; however, this relationship is statistically insignificant.

This study investigates the impact of artificial intelligence (AI) and big data technologies on unemployment in the G7 countries using a dynamic panel estimation approach. The analysis covers the period from 2005 to 2020 and incorporates various control variables related to unemployment rates, along with AI, big data, data science, and machine learning Google Trend Index (GTI). The Arellano–Bover/Blundell–Bond (1998) system estimator is employed to ensure robust results, particularly in cases involving multiple lags of the dependent variable. The most noteworthy results highlight a negative association between AI, big data technologies, and unemployment. These technologies enhance productivity, leading to increased capital accumulation and the creation of new jobs. This validates the "displacement effect" for AI and big data technologies, implying that while certain jobs may be automated, the net effect is job creation. Consequently, implementing AI and big data technologies in economic processes can effectively reduce unemployment rates and boost wages by creating new job opportunities.

In recent years, there has been a rapid increase in the trend towards environmental sustainability through the adoption of renewable energy resources. However, the main concern revolves around whether renewable energy consumption contributes to economic growth. Thus, this study aims to investigate the relationship between renewable energy and economic growth in European countries from 1970 to 2019 using panel data analysis with structural breaks. Additionally, this study demonstrates the influence of renewable energy consumption, capital stock, and the human capital index on economic growth performance in European countries. To control for structural breaks with sharp and smooth changes in the model, Bai & Perron's [10–11] structural breaks test and Sun et al.'s [66] time-varying fixed effects model are applied. The findings indicate that modeling significant structural breaks helps to consider nonlinearity in the model structure, define time dynamics in relationships, and gain new insights. Moreover, new modeling approaches reveal the validity of the time-varying effect of renewable energy on economic growth. Based on the obtained results, this study provides policy implications for governors and researchers.

This study aims to explore gender inequality using spatial panel data models and take a step forward in adopting such a comprehensive approach to identify and estimate the factors driving gender inequality in European countries. The analysis utilizes panel data covers 41 European countries spanning the period from 1990 to 2020. By employing spatial panel data models, the study accounts gender inequality by mainly considering unobserved effects and various other factors, including social, economic, human rights, and environmental degradation. The findings reveal that spatial effects play a significant role in gender inequality, as evidenced by the Gender Inequality Index heatmap and the Moran's I spatial autocorrelation test. Among the panel data regression models estimate to spatial effects, the FE-SAC model is determined to be the most efficient and consistent based on various model selection criteria. The main results show that life expectancy at birth, expected years of schooling, and democracy index have decreasing effects on gender inequality, whereas CO2 emissions per capita have increasing effects. Ultimately, the study concludes that gender equality is linked to unobserved effects such as the same culture, beliefs, and values which are main reasons of spatial effects.

This study examines the determinants of the ecological footprint of production in European countries from 1992 to 2020. Using partial and semipartial correlation analyses and Bayesian Model Averaging (BMA) approach for the first time, the research identifies key variables affecting ecological footprint. Using Bayesian methods, posterior inclusion probabilities (PIPs) were calculated for each variable's coefficient estimates, revealing their relative importance. Biocapacity, energy consumption, industrialization, financial development, life expectancy, and globalization displayed notably high PIPs, indicating their strong influence on the ecological footprint. In addition, the study employs cointegration tests to examine the long-run relationship between ecological footprint and explanatory variables. The results indicate significant cointegration between these variables across panels, supported by various test statistics. In the Weighted Pooled DOLS estimation, biocapacity, energy consumption, and life expectancy significantly influence the ecological footprint, while industrialization, financial development, and globalization exert a comparatively smaller impact. Researchers and policymakers should consider these determinants for effective sustainable development planning. These findings underscore the intricate interplay of factors shaping the ecological footprint and offer insights for effective policy interventions towards sustainable development.

This study examines the relationship between Turkey's airline markets, which responded to local economic shocks, and the USA and Europe airlines market using daily closing stock price data from January 2016 to June 2022. All the variables are non-stationary at level but stationary at first difference. The long-run relationship among the variables is found by employing Maki's (2012) cointegration test considering multiple unknown structural breaks. In addition, the Granger causality analysis results of markets support the correlation of stock prices. The relationship between the global airlines market and Turkey's airlines market is analyzed with different regimes supporting structural breaks. This methodological framework may be considered a significant contribution to financial research.

This study aims to use a monthly dataset from 1991 to 2021 to predict West Texas Intermediate (WTI) oil price dynamics using U.S. macroeconomic and financial factors, as well as a global crisis and crashes. We used advanced machine learning models such as Logistic Regression, Decision Tree, Random Forest, AdaBoost, and XgBoost in this study. According to the results, the XgBoost and Random Forest models outperform traditional models. We also used DeLong statistical test procedures to accurately compare machine learning models' per-formance. In addition, the study used SHAP -SHapley Additive exPlanations values to support model evaluation and interpretability. This new outline highlights the critical features of the WTI crude oil price prediction and provides appropriate model explanations by utilizing the practical SHAP values. The empirical findings showed that machine learning models could successfully and accurately predict the trend of WTI crude oil price changes. Our findings are important for policymakers, companies, and investors, as well as long-term energy-based economic development.

Although large companies try to gain new customers, they also want to retain their old customers. Therefore, customer churn analysis is important for identifying old customers without loss and developing new products and making new strategic decisions for retaining customers. This study focuses on the customer churn analysis, that is a significant topic in banks customer relationship management. Identifying customer churn in banks will helps the management to classification who are likely to churn early and target customers using promotions, as well as provide insight into which factors should be considered when retaining customers. Although different models are used for customer churn analysis in the literature, this study focuses on especially explainable Machine Learning models and uses SHapely Additive exPlanations (SHAP) values to support the machine learning model evaluation and interpretability for customer churn analysis. The goal of the research is to estimate the explainable machine learning model using real data from banking and to evaluate many machine learning models using test data. According to the results, the XgBoost model outperformed other machine learning methods in classifying churn customers.

Turkey attempt to control the fast-rising number of coronavirus cases and deaths since the spread of coronavirus disease 2019 (COVID-19) in every country. Likewise, researchers from different fields have been an effort to explore COVID-19 with distinctive aspects for minimizing the cost of a pandemic on the economy and social life. We know that is impossible reliable and unbiased results of studies without accurate data. Thus, if we gather inadequate data and analysis it, we will be faulty decisions and make policies. For this reason, Benford's Law may be useful for assessing the effects of the current control interventions and may be able to answer the question, ‘‘How flat is flat enough?’’. In this study, we explore whether the COVID-19 data published by Turkey is fake or not with Benford's Law.

Identifying the economic factors that affect economic growth is an important issue for each economy. It is a matter of debate to determine the building blocks of non-oil GDP growth, especially in oil-rich countries, such as Azerbaijan. Using the Fully Modified Ordinary Smallest Square approach between 2005-2019, this study aims to investigate the relationship between real non-oil GDP growth of Azerbaijan and exchange rate and oil prices. Zivot-Andrews unit root test is applied to deal with structural breaks in data and the Gregory-Hansen test for robustness. While conventional unit-root tests decision that the series are not stationary at their level, the Ziwot-Andrews test decision that the series is stationary with structural break. According to the Gregory-Hansen test result, there is a structural break date in the long-run relationship between the real non-oil GDP growth and the oil price and the USD /AZN exchange rate in early 2009. According to FMOLS results, the increase in oil price increases real non-oil GDP growth, and the increase in USD / AZN exchange rate has a decreasing effect on it. This study contains considerable information for future economic policies for oil-rich countries that want to develop the non-oil sector. Keywords: oil price, non-oil GDP, exchange rate, fully modified ordinary smallest square approach, cointegration analysis, Azerbaijan JEL Classifications: C22, E32, E37, Q43 DOI: https://doi.org/10.32479/ijeep.9561

This study investigates the propagation power and effects of the coronavirus disease 2019 (COVID-19) in light of published data. We examine the factors affecting COVID-19 together with the spatial effects, and use spatial panel data models to determine the relationship among the variables including their spatial effects. Using spatial panel models, we analyse the relationship between confirmed cases of COVID-19, deaths thereof, and recovered cases due to treatment. We accordingly determine and include the spatial effects in this examination after establishing the appropriate model for COVID-19. The most efficient and consistent model is interpreted with direct and indirect spatial effects.