University of Mediterranean Karpasia

Intelligent transportation systems increasingly rely on artificial intelligence and predictive analytics to achieve sustainability. This study presents Adaptive Weighting, Chaos Theory, and Gaussian Mutation-based RIME algorithm-tuned Extreme Gradient Boosting (ACGRIME-XGBoost), an advanced Artificial Intelligence (AI)-driven framework specifically designed for carbon emission prediction in air transport to contribute to the development of sustainable smart infrastructure. The proposed hybrid model integrates XGBoost with ACGRIME, a novel metaheuristic optimization algorithm enhanced with chaos theory, adaptive weighting, and Gaussian mutation mechanisms to overcome limitations in traditional hyperparameter tuning approaches. The framework demonstrates exceptional performance on Congress on Evolutionary Computation (CEC) 2020 benchmark functions, outperforming conventional optimization algorithms in accuracy and robustness. When applied to real-world flight data within a smart transportation monitoring, ACGRIME-XGBoost achieves a 94% R2 score for CO2 emission prediction, significantly surpassing other optimized machine learning models. This research bridges the gap between advanced AI optimization techniques and sustainable transportation infrastructure, offering a scalable decision-support system that can be integrated with IoT sensor networks and mobility platforms in the future. The results demonstrate how metaheuristic-assisted machine learning can enhance environmental monitoring capabilities in smart transportation ecosystems, supporting data-driven policy-making for climate-resilient infrastructure and sustainable aviation management within the broader context. Also, the research contributes to sustainable aviation by enabling high-fidelity CO2 prediction models that can inform policy-making and be integrated into digital monitoring tools for future smart transport infrastructures.

The development of renewable energy has emerged as a cornerstone of sustainable economic transformation, offering a pathway to reduce carbon dependence and enhance long-term energy security. As a result, this study examines the influence of supply chain digitalization, economic growth, and environmental stringency policies on renewable energy consumption (REC) across 33 OECD countries from 2000 to 2021. Using the Method of Moments Quantile Regression (MMQR) approach, the research provides robust, distribution-sensitive insights into how these factors shape renewable energy dynamics. In addition to the main variables, financial development and economic globalization were included as control variables to capture broader macroeconomic effects. The empirical results reveal that supply chain digitalization exerts a negative and consistent influence on REC across all quantiles, suggesting that technological advancement within supply chains may still be heavily dependent on non-renewable energy inputs. Conversely, environmental stringency policies demonstrate a positive and significant impact on REC at all quantiles, indicating that stricter environmental regulations effectively drive the transition toward cleaner energy sources. However, the effect of economic growth varies across quantiles, reflecting a nonlinear relationship—fostering renewable energy use in some instances while increasing conventional energy demand in others. Among the control variables, economic globalization enhances REC, implying that greater international integration facilitates technology transfer and access to green innovations. In contrast, financial development negatively affects REC, suggesting that current financial systems may still prioritize fossil fuel investments. Overall, the study emphasizes the need to align digital transformation strategies, financial reforms, and policy frameworks to strengthen renewable energy development and ensure a sustainable, low-carbon future across OECD nations.

Amid increasing job complexity and evolving career demands, understanding how employees can proactively sustain their employability has become a critical concern for organizations. Although prior research highlights the importance of job crafting for employability, the motivational mechanisms through which this relationship unfolds—and the contextual conditions under which it is strengthened or weakened—remain insufficiently understood. Drawing on self-determination theory and role theory, this study examines how job crafting influences sustainable employability through the mediating role of self-determination and the moderating role of role ambiguity. Using a two-wave, time-lagged survey design, data were collected from 989 employees across diverse industries in Türkiye. Job crafting and role ambiguity were measured at Time 1, while self-determination and sustainable employability were assessed one month later. The proposed relationships were tested using confirmatory factor analysis and conditional process analysis. The results show that job crafting is positively associated with both self-determination and sustainable employability. Self-determination partially mediates the relationship between job crafting and sustainable employability, indicating that proactive job redesign enhances employability by fostering autonomous motivation. Moreover, role ambiguity weakens the positive effects of job crafting on both self-determination and sustainable employability, highlighting the importance of role clarity as a boundary condition. This study advances the job crafting and sustainable employability literature by identifying self-determination as a key motivational mechanism and by demonstrating how role ambiguity constrains the benefits of proactive work behavior. By integrating self-determination theory with role theory, the findings offer nuanced insights into how employee agency and contextual clarity jointly support sustainable employability in dynamic work environments.

Environmental sustainability has emerged as a global imperative in the context of accelerating climate change, rapid industrialization, and increasing ecological stress. Ecological quality is necessary for countries to pursue because of its overall benefits to the entire ecosystem. Therefore, due to the significant role that the United Arab Emirates (UAE) plays in the global environment, this research examines the role of Green Innovation (GI), Financial Globalization (FG), Economic Growth (GDP), and Foreign Direct Investment (FDI) in influencing Environmental Quality (EQ) in the UAE from 1991–2022. The UAE is well known for these economic indices. Furthermore, this study employed the innovative Quantile Augmented Dickey–Fuller (QADF) test, Wavelet Quantile Regression (WQR), Wavelet Quantile Correlation (WQC), and Quantile-on-Quantile Granger Causality (QQGC). WQR is able to identify connections between series over a range of quantiles and periods. WQC evaluates the co-movement between variables at different quantile levels and across several scales. The QQGC captures the causal effect of the regressors on EQ. These methods are quite advanced compared to other traditional econometric methods. Based on the outcome of the WQR and WQC methods, evidence shows that GI contributes to EQ across all quantiles in the short, medium, and long term, while FG, GDP, and FDI reduces EQ across all quantiles in the short, medium, and long term. The QQGC results also affirm causality among the variables, implying that GI, FG, GDP, and FDI can predict EQ across all quantiles. This research recommends that the UAE should improve on its environmental policies both domestically and internationally by making them more stringent, and continue to promote clean energy investments.

Abstract The high rigidity and load‐bearing capacity of concrete‐filled stainless steel‐tube (CFSST) structures make them a common choice for high‐rise building construction. CFSST columns exhibit intricate interactions between concrete and stainless steel, leading to challenges in accurately modeling their behavior under axial loading conditions. Stainless steel tubular circular columns with axially loaded concrete and ultra‐high strength concrete are predicted to perform using a hybrid technique. The proposed hybrid strategy is the collaborative implementation of Attention Pyramid Convolutional Neural Network (APCNN) and Giant Trevally Optimizer (GTO). It is hence called the APCNN‐GTO technique. The primary goal of the proposed approach is to increase the CFSST columns' ultimate axial load‐carrying capability in comparison to conventional reinforced concrete (RC). The data is initially extracted from the dataset of steel tubular columns filled with concrete. They then feed the data into a pre‐processing approach based on federated neural collaborative filtering (FedNCF). To extract the best features, the dual‐tree biquaternion wavelet transform (DTBWT) is used for the pre‐processing result. For circular CFSST columns subjected to axial loading, the ultimate axial load is predicted using the APCNN technique. Next, APCNN's weight parameters are tuned with GTO. The proposed technique's implementation in the MATLAB platform is described as follows: the proposed solution outperforms existing approaches such as CNN, FNN, and RNN by 98% and has a 0.1% lower error rate, paving the path for advanced applications in high‐rise and heavy‐load construction.

The banking sector faces increasing pressure to balance financial performance with sustainability goals amid ongoing digital transformation, regulatory reform, and societal expectations for ethical responsibility. Entrepreneurial leadership has emerged as a pivotal approach for addressing these challenges; however, the behavioral and contextual mechanisms through which it shapes sustainability remain insufficiently understood. Drawing on Social Learning Theory (SLT), this study investigates how and when entrepreneurial leadership enhances sustainable bank performance through the mediating role of employee creativity and the moderating influence of an innovation-oriented climate. A two-wave multi-source survey was conducted among 459 employees and managers from Turkish banks, and the hypothesized model was tested using structural equation modeling to ensure robust empirical validation. The results indicate that entrepreneurial leadership significantly fosters employee creativity, which serves as a critical behavioral mechanism linking leadership behaviors to sustainability-oriented outcomes. Moreover, an innovation-oriented climate strengthens both the direct effect of entrepreneurial leadership on creativity and its indirect effect on sustainable bank performance, emphasizing the contextual importance of supportive organizational environments. Theoretically, this study extends the leadership and sustainability literature by illustrating how learning and behavioral modeling processes translate leadership vision into sustainable performance. Practically, it offers actionable guidance for bank executives to develop innovation-oriented climates, empower employees’ creative engagement, and design incentive systems that align leadership behavior with sustainability imperatives, thereby enhancing resilience and long-term competitiveness.

The incorporation of environmental, social, and governance (ESG) concerns into corporate finance has accelerated globally; nevertheless, empirical data about its effects in the U.S. context is still scarce. This research examines the impact of sustainable financing on the financial performance of non-financial enterprises listed on the New York Stock Exchange (NYSE) from 2008 to 2024. This study used the stakeholder theory and other theories to analyze four aspects of sustainable finance: green financing efforts, emission reduction strategies, sustainable product initiatives, and environmental investment initiatives. The study implemented a dynamic panel regression model with the two-step Generalized Method of Moments (GMM) to mitigate endogeneity and omit variable bias. The findings indicate that green finance, emission reduction strategies, and sustainable product efforts have a positive and significant impact on Return on Assets (ROA) and Return on Net Operating Assets (RNOA), demonstrating their effectiveness in enhancing financial performance. Conversely, environmental investment programs exhibited a strong and negative correlation with financial success, indicating immediate cost implications. These findings emphasize the significance of strategic planning in sustainability investments and reinforce the necessity for legislative incentives to assist enterprises throughout the transition. This study enhances the literature by providing U.S.-specific, component-level insights into the financial implications of sustainable financing, therefore offering pragmatic counsel for managers, investors, and regulators.

This study examines how Industry 4.0 (I4.0) technologies enhance supply chain resilience (SCR) in manufacturing firms by testing the mediating roles of supply chain agility (SCAG), supply chain adaptability (SCAD) and the moderating effect of customer integration (CI). Grounded in the Resource-Based View (RBV) and Dynamic Capabilities View (DCV), the research conceptualizes digital technologies—such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI)—as both strategic resources and enablers of dynamic capabilities in turbulent environments. Survey data were collected from 273 manufacturing firms in Turkey, a context shaped by geopolitical and economic disruptions, and analyzed using structural equation modeling (SEM). The results indicate that I4.0 technologies positively affect SCR directly and indirectly through SCAG and SCAD. However, while agility consistently strengthens resilience, adaptability shows a negative mediating effect, suggesting context-specific constraints. CI significantly amplifies the positive impact of I4.0 on SCR, underscoring the importance of external relational capabilities. Theoretically, this research advances supply chain literature by integrating RBV and DCV to explain how digital transformation drives resilience through distinct dynamic capabilities. Practically, it offers guidance for managers to combine digital infrastructure with collaborative customer relationships to mitigate disruptions and secure long-term performance. Overall, the study provides an integrated framework for building resilient supply chains in the digital era.