Search Results (352)

This study investigates the dynamic relationship between interest and noninterest income at Azer Turk Bank using quarterly data from 2016Q1–2024Q3. Unit root tests including Augmented Dickey–Fuller (ADF), Kwiatkowski–Phillips–Schmidt–Shin (KPSS), and Fourier–KPSS indicate that both variables are non-stationary in levels but become stationary after first differencing. The Hylleberg–Engle–Granger–Yoo (HEGY) test further shows that both series contain a unit root at the non-seasonal (0) frequency, while no unit roots are detected at the seasonal frequencies (π/2 and 3π/2). Johansen cointegration and the Fourier Autoregressive Distributed Lag (Fourier–ADL) framework confirm the existence of a stable long-run equilibrium. As a key methodological contribution, the study derives explicit Fourier-based Vector Error Correction Model (VECM) equations, enabling the modeling of cyclical deviations around nonlinear trends. Fourier Toda–Yamamoto and Breitung–Candelon frequency-domain causality tests reveal asymmetry: interest income consistently drives noninterest income in the short and medium run, whereas the reverse effect is weak. The results also confirm mean reversion, with deviations from equilibrium corrected within 5.9; 2.5 quarters. Overall, the findings highlight the limited diversification potential of noninterest income and the decisive role of lending in bank revenues, offering both methodological advances and practical guidance for macroprudential policy. Full article

South Asia faces the dual challenge of sustaining rapid economic growth while managing severe ecological pressures. This study explores the relationship between Ecological Footprint (EF), Financial Development (FD), Economic Growth (GDP), Foreign Direct Investment (FDI), and Political Stability (PS) in SAARC countries from 2000 to 2020. Using a Panel Vector Autoregression (PVAR) combined with a Vector Error Correction Model (VECM), the analysis captures both short-run dynamics and long-run equilibrium relationships, addressing endogeneity among variables. Results reveal that EF negatively correlates with FD, GDP, and FDI, while showing a positive association with PS. Cointegration tests using dynamic and fully modified ordinary least squares confirm long-term relationships between the variables. Impulse response functions illustrate how shocks to one variable affect others over time, highlighting complex interactions. Granger causality tests suggest limited short-term causal links, reflecting the multifaceted nature of these relationships. This research is particularly relevant as SAARC countries face the dual challenge of sustaining rapid economic growth while mitigating ecological pressures. The study advances the literature by explicitly integrating political stability into the environmental–economic nexus, a factor often overlooked in earlier regional analyses. By providing empirical evidence on the joint role of economic, financial, and political drivers of ecological sustainability, the paper contributes both to academic debate and to the design of more balanced policy frameworks for sustainable development in South Asia. Full article

Decent work is essential for fostering workers’ professional and personal growth, as well as for guaranteeing social security and welfare through the enforcement of rules and regulations. Recently, the global labour market has been profoundly influenced by technological innovations, the growth of the services sector, and globalization. Consequently, the protection of fundamental workers’ rights has become increasingly important, establishing that decent employment is crucial for generating superior and higher-quality output. In the Gulf Cooperation Council countries, there is an increasing necessity to acknowledge the significance of decent work conditions for sustained economic development. This study aims to examine the influence of decent work determinants on sustained economic development from 1991 to 2022. The analysis employs panel data methodologies, specifically cross-sectionally Augmented Autoregressive Distributed Lag models, alongside robustness assessments utilising Driscoll–Kraay standard errors, Augmented Mean Group, and Common Correlated Effects Mean Group estimators, revealing that GDP per employee exerts a significant and consistent positive influence on economic growth. Conversely, other aspects of decent work, including unemployment, vulnerable employment, and self-employment, do not have statistically significant long-term consequences. The Westerlund ECM cointegration test verifies the lack of a long-term equilibrium link between decent work indices and economic development. The findings indicate that although labour market quality is significant, productivity is the primary catalyst for sustained growth in the GCC setting. Policymakers should prioritise productivity-enhancing changes within comprehensive employment and labour market strategies. Full article

This study proposes a novel causality-structured matrix long short-term memory (C-mLSTM) model for significant wave height (SWH) forecasting. The framework incorporates a two-stage causal feature selection methodology using cointegration testing and Granger causality testing to identify long-term stable causal relationships among variables. These relationships are embedded within the C-mLSTM architecture, enabling the model to effectively capture both temporal dependencies and causal information within the data. Furthermore, the model integrates Bayesian optimization (BO) and twin delayed deep deterministic policy gradient (TD3) algorithms for synergistic optimization. This combined TD3-BO approach achieves an 11.11% improvement in the mean absolute percentage error (MAPE) on average compared to the base model without optimization. For 1–24 h SWH forecasts, the proposed TD3-BO-C-mLSTM outperforms the benchmark models TD3-BO-LSTM and TD3-BO-mLSTM in prediction accuracy. Finally, a Shapley additive explanations (SHAP) analysis was conducted on the input features of the BO-C-mLSTM model, which reveals interpretability patterns consistent with the two-stage causal feature selection methodology. This research demonstrates that integrating causal modeling with optimization strategies significantly enhances time-series forecasting performance. Full article

This study examines the dynamic relationship between the gold markets of India and the United States from 2005 to 2025. Recognising gold’s role as a hedge and safe-haven during market uncertainty, we employ the Autoregressive Distributed Lag (ARDL) model to assess long-term co-integration and apply the Toda–Yamamoto causality test to evaluate directional influences. Additionally, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1) model is applied to examine volatility spillovers. Results reveal no long-term co-integration between the two markets, suggesting they function independently over time. However, unidirectional causality is observed from the U.S. to the Indian gold market, and the GARCH model confirms bidirectional volatility transmission, indicating interconnected short-run dynamics. These findings imply that gold market shocks in one country may affect short-term pricing in the other, but not long-term trends. From a portfolio diversification and risk management perspective, investors may benefit from allocating assets across both markets. This study contributes a novel empirical framework by integrating ARDL, Toda–Yamamoto Granger causality, and GARCH(1, 1) models over a two-decade period (2005–2025), incorporating post-COVID market dynamics. The combination of these methods, applied to both an emerging (India) and developed (U.S.) economy, provides a comprehensive understanding of gold market interdependence. In doing this, the paper offers valuable insights into causality, volatility transmission, and diversification potential. The econometric rigour of the study is enhanced through residual diagnostic tests, including tests of normality, autocorrelation, and other heteroscedasticity tests, as well as VAR stability tests. These ensure strong inference and model validity; more specifically, they are pertinent to the analysis of financial time series. Full article

Both developed and developing countries increased their energy consumption while continuing to advance economically and financially. In parallel with increasing energy use, the intensification of anthropogenic activities has led to higher greenhouse gas emissions, exposing countries to the challenges of climate change and global warming. The environmental degradation resulting from rapid growth in both developed and emerging economies has drawn the interest of scholars, policymakers, and environmental advocates. This study aims to address the relationships between financial development, economic growth, energy consumption, and environmental degradation in G7 and E7 countries. Within this framework, panel cointegration and causality analyses were conducted using annual data from the period between 2000 and 2021 for the relevant countries. The results of the cointegration analysis indicate that the variables move together in the long run in both groups of countries. Furthermore, the long-term relationship coefficients reveal that economic growth and energy consumption contribute to environmental degradation in both G7 and E7 nations. Moreover, the results show that, unlike in E7 countries, financial development in G7 countries exacerbates environmental degradation, while trade openness mitigates it. Panel causality analysis reveals that in E7 countries, changes in financial development influence CO₂ emissions, and variations in CO₂ emissions, in turn, affect economic growth and trade openness. In G7 countries, the analysis results indicate a bidirectional causal relationship between trade openness and CO₂ emissions across the panel. The panel cointegration and causality analyses yield differing results at the country level. Given these findings, it can be recommended that both G7 and E7 countries transition from fossil fuel sources to clean energy sources in conducting economic activities, promote green economy initiatives, and expand the use of green finance instruments to mitigate environmental degradation. Full article

This study examines the long-run relationship between carbon dioxide (CO₂) emissions and key macroeconomic and sectoral drivers in ten ASEAN economies from 1995 to 2023. Employing Driscoll–Kraay standard errors, Prais–Winsten regression, heteroskedastic panel-corrected standard errors, Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) estimators, the analysis accounts for cross-sectional dependence, slope heterogeneity, and endogeneity. Results indicate that GDP exerts a more-than-unitary positive effect on emissions, with a negative GDP-squared term supporting the Environmental Kuznets Curve. Agriculture raises emissions through land-use change and high-emission cultivation practices, while tourism shows a negative association likely reflecting territorial accounting effects. Trade openness increases emissions, highlighting the carbon intensity of export structures, whereas foreign direct investment exerts no significant net effect. These results suggest that ASEAN economies must accelerate renewable energy adoption, promote climate-smart agriculture, embed enforceable environmental provisions in trade policy, and implement rigorous sustainability screening for FDI to achieve low-carbon growth trajectories. Full article

Soaring housing prices worldwide are compromising housing affordability, potentially leading to significant economic, social, and health repercussions. Understanding the price discovery process within the real estate market is therefore crucial for policymakers. While the relationship between land and housing prices in urban residential markets has been widely examined in the literature, the results are often context-specific, leaving the question of whether the land market leads the housing market or vice versa open to debate. Saudi Arabia, with its rapidly growing real estate market, evolving demographics and urbanization trends, and transformative regulatory reforms, presents a compelling context for revisiting the land–housing nexus. This study examines the long-term relationship between land and housing markets and investigates the short-term price dynamics with the ultimate goal of understanding the price formation in the housing market. The study dataset comprises quarterly time-series price indices published by the General Authority for Statistics (GASTAT) in Saudi, representing the nation-wide price movements of residential lands and villas from 2014Q1 to 2025Q1. The study employs the Johansen cointegration method and the Granger causality testing. The results of cointegration analysis confirm a significant long-run equilibrium relationship between the two markets, while the error correction model reveals that both land and housing prices adjust to restore this equilibrium. Granger causality test results show a unidirectional relationship, where land prices predict future housing prices, consistent with the neoclassical rent theory. These findings reinforce the long-term, intrinsic link between land and housing markets observed in prior studies. The dynamics in the Saudi market are likely shaped by rapid urbanization that intensified speculation in the land market, and also the prevalence of self-building enabled by government-supported financing. This study underscores the importance of striking a delicate balance between supply and demand side policies in the real estate market while monitoring the impact of these policies on housing affordability. Full article

Considering the contemporary, rapidly evolving society, renewable energy emerges as a key element in advancing both environmental resilience and energy independence. The current study aims to undertake a comparative analysis of the renewable energy adoption between the Old Member States (OMSs) and New Member States (NMSs) of the European Union (EU). This study focuses on regional heterogeneity as well as the role of economic, social, and environmental determinants in shaping effective energy transition policies. This study uses advanced long-term panel estimates such as Dynamic Ordinary Least Squares (DOLS), Fully Modified Least Squares (FMOLS) and Canonical Cointegration Regression (CCR) on a dataset covering the 2010–2023 period. Moreover, this study utilizes quantile regression methods such as Quantile Regression (QREG) and Method of Moments Quantile Regression (MMQR). Finally, this study employs the Dumitrescu–Hurlin test to assess panel causality. The empirical findings reveal notable discrepancies between the two samples when it comes to fossil fuel reliance, income inequality, financial and economic development, the existing level of greenhouse gas emissions, and green finances influencing renewable energy adoption. In the OMS region, a 1% increase in GHG and income inequality reduces the adoption of renewable energy by 0.80–1.14% and 0.61–0.67%, respectively, while a 1% increase in GDP increases the adoption of renewable energy by 0.72–0.92%. In the NMS region, GHG inhibits renewable energy transition by 0.27–0.30%, while fossil fuel energy share, income inequality, green finance, GDP and financial development do not have a significant effect. These results highlight economic development as the key to renewable energy transition in OMSs, while in NMSs, GHG and financial development are key levers. This research seeks to support the developing and restructuring of the existing green framework to enhance its overall effectiveness. Full article

The relationship between renewable (REN) and non-renewable (NREN) energy and economic growth plays a fundamental role in sustainable development. The number of studies on this relationship in countries with the highest REN consumption is limited. This study analyzes the effects of REN and NREN on economic growth between 1970 and 2023, focusing on the ten leading countries in REN consumption. These countries constitute an appropriate sample for analysis, not only due to their high REN capacity but also because they represent diverse levels of economic development. For this purpose, second-generation panel data methods were employed to investigate the long-run effects, taking into account cross-sectional dependence and heterogeneity in the dataset. The CADF unit root test developed by Pesaran indicated that all variables are stationary at their first differences. The Westerlund panel cointegration test confirmed the existence of a long-run relationship among the variables. Long-run coefficients were estimated using the Common Correlated Effects Mean Group (CCE) approach developed by Pesaran and the Augmented Mean Group (AMG) estimators proposed by Bond & Eberhardt and Eberhardt & Teal. The results revealed that renewable energy consumption has a positive and significant effect on economic growth, while fossil fuel consumption continues to have a favorable effect on growth. However, the negative and significant effect of primary renewable energy production suggests that technological deficiencies and efficiency problems in current production structures may play a role. Overall, this study highlights the necessity of aligning energy policies with both environmental sustainability and economic growth objectives. Full article

This study examines the influence of economic growth, energy consumption, a shadow economy, and foreign direct investment (FDI) on the ecological footprint in ASEAN countries. The analysis covers a panel of nine member states—Brunei, Cambodia, Indonesia, Lao PDR, Malaysia, the Philippines, Singapore, Thailand, and Vietnam—over the period from 1993 to 2017 due to data availability. To ensure robustness, various panel econometric techniques were employed, including cross-sectional dependence, panel unit root, and cointegration tests, as well as estimation methods such as Driscoll–Kraay standard errors, feasible generalized least squares (FGLS), and panel-corrected standard errors (PCSE). The results do not support an inverted U-shaped Environmental Kuznets Curve (EKC) between economic growth and ecological footprint in the ASEAN countries. Moreover, the findings consistently show that energy consumption, the size of the shadow economy, and FDI exert a statistically significant and positive impact on the ecological footprint towards the Driscoll–Kraay standard errors, FGLSs, and PCSE estimators. For policy recommendations, a country’s pursuit of economic growth should be aligned with a higher degree of environmental sustainability by strategically reducing energy consumption, curbing the shadow economy, and managing foreign direct investment responsibly. Full article

Controlling dissolved nitrogen is critical to meeting increasingly stringent steel quality targets, yet the variable kinetics of gas absorption and removal across production stages complicate real-time decision-making. Leveraging a total of 291 metal samples, the research applied ordinary least squares (OLS) regression, enhanced by cointegration diagnostics, to develop four stage-specific models covering pig iron after desulfurization, crude steel in the basic oxygen furnace (BOF) before tapping, steel at the beginning and end of secondary metallurgy processing. Predictor selection combined thermodynamic reasoning and correlation analysis to produce prediction equations that passed heteroscedasticity, normality, autocorrelation, collinearity, and graphical residual distribution tests. The k-fold cross-validation method was also used to evaluate models’ performance. The models achieved an adequate accuracy of 77.23–83.46% for their respective stages. These findings demonstrate that statistically robust and physically interpretable regressions can capture the complex interplay between kinetics and the various processes that govern nitrogen pick-up and removal. All data are from U. S. Steel Košice, Slovakia; thus, the models capture specific setup, raw materials, and production practices. After adaptation within the knowledge transfer, implementing these models in process control systems could enable proactive parameter optimization and reduce laboratory delays, ultimately minimizing excessive nitrogenation in finished steel. Full article

The integration of artificial intelligence (AI) into environmental, social, and governance (ESG) disclosure represents a critical frontier for corporate transparency in emerging markets. This study investigates the relationship between AI adoption in ESG reporting, disclosure quality, and financial performance among 180 Saudi-listed companies (2021–2024) within Vision 2030’s transformative context. Using the System Generalized Method of Moments (GMM) estimation with panel unit root and cointegration testing to ensure stationarity assumptions and addressing endogeneity through bounding analysis, the study finds that AI adoption intensity significantly enhances ESG disclosure quality (β = 0.289, p < 0.001), with coefficient significance assessed through t-tests using firm-clustered robust standard errors. Enhanced disclosure quality translates into meaningful financial performance improvements: 0.094 percentage points in return on assets (ROA), 0.156 in return on equity (ROE), and 0.0073 units in Tobin’s Q. Mediation analysis reveals that 73% of AI’s total effect operates through improved ESG quality rather than direct operational benefits. The findings demonstrate parametric bounds robust to macroeconomic confounders, suggesting AI-enhanced transparency creates substantial shareholder value through strengthened stakeholder relationships and reduced information asymmetries. Full article

This study examines the impact of renewable energy, economic growth, and trade openness on CO₂ emissions in the EU-15 countries over the period 1980–2022, employing the ARDL modeling framework. In addition, a panel PMG-ARDL model is employed as a robustness check. The analysis identifies cointegration among the variables in 11 out of the 15 countries studied. Economic growth is found to increase CO₂ emissions, highlighting the ongoing challenge of aligning economic expansion with environmental objectives. The estimated coefficients for economic growth range from 0.43 to 5.70, depending on the country. Renewable energy significantly reduces emissions, highlighting its critical role in achieving sustainability (the corresponding coefficient moves in the range −0.13 to −0.96). Trade openness generally shows a neutral impact on emissions across most cases. Overall, renewable energy contributes to reducing CO₂ emissions, whereas the effects of economic growth and trade openness remain mixed and country-specific. These findings highlight the need to promote cleaner technologies, enhance energy efficiency, and ensure broader access to environmentally friendly energy sources. Full article

Understanding the dynamics and drivers of groundwater storage (GWS) is crucial for sustainable resource management. Most studies attribute GWS changes to climate change or human activities, often neglecting external hydrological influences. In this study, we categorize the driving factors influencing GWS changes into three groups: climate change, human activity, and regional hydrological pressure. We emphasize that the coupling effects and potential disturbances from adjacent hydrological systems may significantly affect local groundwater evolution. This perspective differs from conventional approaches that focus solely on local factors. This study analyzes the spatiotemporal evolution of GWS in Shanxi Province, located in the eastern Loess Plateau, from 2003 to 2023 using GRACE and GLDAS data. We examine the linkage between GWS in Shanxi and the North China Plain through correlation analysis, Engle–Granger cointegration tests, and Granger causality tests. The results show that GWS in Shanxi showed an average annual reduction of −17.27 ± 1.4 mm/yr, with the most severe depletion occurring in the southeastern region, which is geographically adjacent to the North China Plain. The results of the Engle–Granger cointegration test and Granger causality analysis reveal a bidirectional causal relationship between GWS changes in the two regions, indicating that changes in GWS in either region may have a significant impact on the other. The results of the contribution analysis indicate that the North China Plain’s groundwater decline contributes approximately −53.89% to the reduction of GWS in Shanxi, while human activities and external hydrological influences together explain over 98% of the change. This result suggests that relying solely on climatic and human activity factors to explain groundwater changes may lead to significant biases, as ignoring interregional hydrological linkages can amplify or obscure the attribution of local groundwater variations, resulting in distorted conclusions. These findings highlight the value of remote sensing in capturing regional hydrological interactions and underscore the need to integrate interregional groundwater connectivity into policy design for sustainable groundwater governance. Full article