As climate change and resource scarcity intensify, the pursuit of green economic growth has become a central policy priority across Europe. Understanding how different factors, particularly digitalization, institutional adaptation, and technological progress, interact to shape eco-productivity is essential for guiding sustainable transformation. This study explores the evolving dynamics of green economic growth across European countries, emphasizing the importance of technological change, institutional adaptation, and digital integration in driving sustainable development. While the literature has advanced the measurement of eco-productivity, gaps remain in understanding how digital capital interacts with traditional productivity models and environmental efficiency components. This research aims to address these gaps by evaluating green growth trajectories via a combination of entropy-based undesirable composite indicators and total factor productivity change (TFPCH) under both baseline and digital model specifications. Using panel data from 2005-2023, the study applies decomposition techniques to assess technological progress (TECH) and efficiency change (TECCH), complemented by nonparametric tests to determine statistical significance. The results show a general decline in environmental burdens across most EU countries, alongside modest but heterogeneous improvements in green productivity. While the TFPCH indicator remained stable between models, notable divergences emerged in its components: the digital model reported lower TECH values, indicating slower frontier advancement, but higher TECCH values, suggesting stronger catch-up dynamics when digital capital is accounted for. Countries such as Sweden, Finland, and Estonia led in reducing undesirable outputs, whereas others, including Poland, Ireland, and Ukraine, presented persistent challenges in improving relative eco-efficiency. These findings contribute to a deeper understanding of the structural factors underlying green growth and reveal the transformative potential of digitalization when appropriately integrated. The study concludes that while green economic growth is underway in many European regions, its pace and composition vary widely, requiring targeted policy responses.

The contemporary stage of technological development is characterised by a transition from the digital economy to a cognitive–virtual phase, in which technology serves as a key instrument for achieving the Sustainable Development Goals (SDGs). In a broad context, this paper conceptualises technological development as a cyclical process that reflects the regularities of successive technological paradigms, economic waves, and industrial revolutions. Particular attention is given to the integration of approaches from economics, management, mathematics, information technology, and the social sciences to enable a systemic analysis of sustainable development phenomena. The literature review demonstrates that, despite extensive research on technological paradigms and digital transformation, the interrelation between technological cycles and SDGs across micro-, meso-, macro-, and mega-levels remains insufficiently examined. Moreover, a coherent conceptual model explaining how the wave structure of technological progress contributes to the resilience of economic and social systems has not yet been fully developed. The objective of this study is to establish a conceptual framework that unites the theory of economic cycles, the evolution of technological paradigms, and the sustainable development agenda within the paradigm of Virtual Economics. Methodologically, the study adopts an interdisciplinary approach combining elements of mathematical modelling, systems analysis, and comparative–historical methods. The findings suggest that technological cycles not only reflect the internal dynamics of innovation but also shape the conditions for achieving SDGs through digitalisation, cognitive technologies, and the development of virtual ecosystems. The study concludes that Virtual Economics represents a new integrative platform combining economic, technological, and socio-cultural dimensions of sustainable development. Future research perspectives involve examining the role of the metaverse as a cognitive-economic domain of the seventh technological paradigm and developing mathematical models to forecast its impact on global processes.

Cryptocurrency markets are highly volatile, creating challenges for accurate risk management and forecasting. As digital assets become more integrated into financial systems, understanding their volatility dynamics is essential for investors and policymakers. Previous research has primarily applied standard GARCH models to cryptocurrencies, often neglecting advanced specifications that capture asymmetry, regime-switching, and long-memory effects. This limits the accuracy of volatility forecasts and fails to reflect the unique behaviour of digital assets. This study aims to identify the most effective GARCH-class models for forecasting volatility in Bitcoin, Ethereum, Binance Coin, and Ripple. We analyse daily returns from August 2017 to December 2024, applying eight advanced GARCH specifications: EGARCH, GJR-GARCH, FIGARCH, HYGARCH, MSGARCH, CS-GARCH, and Log-GARCH. Hyperparameter tuning is conducted via grid search across lag orders (p, q ∈ [1, 5]), mean equations, and error distributions. Model performance is evaluated using AIC, BIC, RMSE, and MAE. Results show that MSGARCH and EGARCH outperform symmetric and short-memory models, highlighting the importance of regime-switching and leverage effects. FIGARCH provides the best fit for Bitcoin and Ethereum, confirming long-memory persistence. Skewed Student’s t and GED distributions improve accuracy by capturing heavy tails and asymmetry. These findings demonstrate the limitations of standard GARCH models and underscore the value of advanced specifications in modelling cryptocurrency volatility. The study offers practical insights for traders and risk managers, contributing to more robust forecasting in non-stationary markets. Advanced GARCH models significantly enhance volatility prediction for digital assets. Future research could extend this framework to other speculative instruments or integrate machine learning techniques to further improve performance.

The accelerating transition toward knowledge-based and innovation-oriented economies has intensified the need to assess how innovation-driven supply factors influence economic growth and innovation-development security. Although many studies have examined the relationship between innovation, productivity, and competitiveness, important research gaps remain, particularly regarding the quantitative assessment of innovation-driven macroeconomic factors within the aggregate supply framework and their direct contribution to innovation security as an element of national economic resilience. Furthermore, existing international evaluation approaches, including the European Innovation Scoreboard, do not adequately capture systemic sustainability, vulnerability thresholds, or stability margins of innovation systems. Addressing these limitations, this study provides a scientifically grounded quantitative evaluation of the contribution of classical aggregate supply macro factors and innovation-related macro factors to economic growth, while operationalising resulting indicators of innovation-development security for comparative assessment across potential (Ukraine, Georgia), actual (Poland), and former (United Kingdom) EU member states. A neoclassical Cobb–Douglas production function embedded within a Keynesian analytical framework is applied, incorporating Hicks-neutral technical progress, constant returns to scale, and diminishing marginal productivity. Innovation drivers—such as gross domestic expenditure on R&D, innovation expenditure, and total education expenditure—are integrated into a dynamic model establishing causal relationships between inputs and outputs without requiring extended time-series datasets. The Solow residual methodology formalises the contribution of innovation and classical macroeconomic determinants to total factor productivity and economic growth, while key innovation security indicators are proposed, supported by defined safe-existence boundaries. The results demonstrate a substantial and heterogeneous influence of innovation inputs on growth dynamics, resilience, and systemic stability, revealing structural asymmetries and potential vulnerabilities. The study concludes that innovation is not only a fundamental driver of economic development but also a crucial determinant of innovation-development security, offering policymakers analytical tools for monitoring resilience, identifying risks, and strengthening long-term competitiveness.

This study investigates the economic, sociological, and political consequences of Donald Trump’s 2025 protectionist trade policies, aiming to understand how public experiences influence attitudes toward international cooperation. It addresses gaps in existing research by revealing that negative policy experiences may paradoxically strengthen support for multilateralism. The analysis is contextualized within accelerating scientific and technological progress, particularly developments in information and communication technologies, arguing that forces once driving hyper-globalization now increasingly contribute to selective deglobalization. A convergent parallel mixed-methods design is applied, combining a systematic literature review of 160 sources (2016–2025) with an original survey of 209 respondents from 17 countries. Data were analysed using descriptive statistics, Pearson’s correlation coefficients, and thematic analysis to explore relationships between economic consequences, social attitudes, and political preferences. Results reveal a significant “cooperation paradox:” among respondents reporting negative national impacts from Trump’s policies (67.9%), support for international cooperation remains very high (85.2%), with a statistically significant negative correlation between perceived national harm and opposition to cooperation (r = –0.20, p < 0.01). The study also identifies a “macro–personal disconnect,” where perceptions of national economic harm exceed reported personal financial deterioration (r = –0.44, p < 0.001). These findings provide the first empirical evidence of this cooperation paradox, challenging theories linking negative economic experiences with isolationism. The study introduces selective deglobalisation as a theoretical concept describing rational public learning, where harmful policies are rejected while commitment to revised multilateral frameworks is reinforced. Integrating technological transformation into this analysis deepens understanding of public opinion on globalisation and offers valuable insights for policymakers.

This study explores the intersection of artificial intelligence and economic modeling by extending the classical Cobb–Douglas production function into a custom neural network architecture implemented in TensorFlow. Motivated by the growing emphasis on sustainable development and its often ambiguous role in economic performance, the research addresses a gap in existing literature: the lack of integrated models that quantify the effect of Sustainable Development Goals (SDGs) within production functions. While previous studies have assessed SDGs and productivity separately, few have embedded sustainability metrics directly into core economic frameworks alongside traditional inputs like capital and labor. To fill this gap, the proposed model features trainable subcomponents for total factor productivity (TFP), physical capital, human capital, and SDG-related factors. Key coefficients—including capital elasticity (α), labor elasticity (β), and an SDG penalty term (γ)—are optimized using gradient descent. Experimental results reveal that while SDG constraints can initially appear to limit economic output, the model identifies conditions under which specific SDG factors contribute positively to productivity. To manage this duality, a three-level AI-based regulatory mechanism is introduced: (1) post-training SDG weighting based on their marginal output contribution, (2) filtering of influential SDG indicators via the Pareto principle, and (3) architectural separation of SDG pathways with controlled activation. These innovations enhance the interpretability and efficiency of sustainability-aware economic forecasting. The findings not only challenge the assumption of a trade-off between growth and sustainability but also suggest that targeted regulation of sustainability inputs can optimize outcomes. Future work may expand this framework to sector-specific models or broader macroeconomic simulations.

The growing complexity of managerial phenomena and the interdisciplinary nature of management science call for more coherent methodological frameworks for theory building and model testing. While management science draws from both technical and social disciplines, its theoretical development remains fragmented and often lacks methodological integration. Previous studies have highlighted inconsistencies between paradigmatic assumptions, modelling strategies, and empirical validation, particularly in the treatment of latent variables. Despite the broad adoption of structural equation modelling (SEM), its potential for bridging theory and empirical evidence in dynamic and virtual contexts remains underexplored. This study aims to conceptualise an integrative approach that connects paradigmatic foundations, structural modelling, and simulation-based testing within virtual environments. By analysing classical and contemporary management paradigms — including functionalist, interpretative, critical, and postmodern perspectives — the paper identifies methodological challenges in aligning theoretical reasoning with empirical analysis. The methodological framework combines SEM procedures, such as confirmatory factor analysis and model fit evaluation, with the use of virtual and simulation-based environments as experimental spaces for theory validation. The findings emphasise that virtual environments enable real-time experimentation, allowing for the iterative refinement of theoretical models under controlled digital conditions. These spaces enhance the reflexivity and reproducibility of management research by enabling researchers to observe the interaction between theoretical constructs and simulated organisational processes. The integration of SEM with simulation techniques demonstrates how theoretical constructs can be dynamically tested and adjusted through virtual modelling. The study concludes that theory building in management science benefits from combining paradigmatic reflection, structural modelling, and virtual experimentation. This triadic framework promotes methodological pluralism, computational adaptivity, and deeper theoretical coherence, suggesting that virtualised modelling environments may become essential tools for future management research and education.

The article’s subject is the state of the United Arab Emirates, established in 1971 in a primarily desert area with approximately 180,000 inhabitants, almost entirely Arab. Today, the country is home to over 10 million people from over 200 nations and is undergoing a massive transformation. However, to avoid the consequences of becoming dependent on oil revenues, the UAE (top oil producer) is implementing economic diversification programs. This process is accompanied by a dynamic development of the state’s activity in space. The research objective is to examine the UAE’s space sector’s phenomenon and diplomatic activity, which impacts the country’s economic diversification. Consequently, research questions were posed: what initiatives can significantly contribute to the development of the UAE space industry, nonoil export, and, consequently, to economic diversification? What international space programs should be crucial for diplomacy? At the epistemology level, within the theoretical framework, the paper applied international political economy to explain the issue of diversification. Meanwhile, economic development theory focuses on production, consumption, and trade changes. In its light, the article discusses the rentier state theory, the concept of the resource curse, and the “Dutch disease” syndrome. To find answers to the research questions, appropriate empirical methods were used: quantitative research, statistical analyses, observation, including participant observation, and case studies. Additionally, the Herfindahl Hirschman Index (HHI), the Dickey-Fuller test (ADF), the autoregression model (VAR), the Johansen cointegration test, the vector error correction model (VECM), and the Granger causality test were applied. The methods contributed to obtaining the results and finding answers to the research questions. Hypothesis validation shows that numerous initiatives of the United Arab Emirates to build the space industry have not yet impacted economic diversification, but they have great future potential.

This study aims to forecast future entrepreneurial and employability opportunities in the United Arab Emirates (UAE) through an analysis of Government Finance Statistics. Specifically, it examines the impact of transactions in non-financial assets, financial assets, and liabilities on the gross operating balance to identify potential areas for business growth. The research explores various business activities, including investments in non-financial assets, lending and borrowing, acquisition of financial assets, and the incurrence of liabilities. Using a time series analysis approach, this study employs quarterly data from 2012 Q1 to 2023 Q3 to estimate short- and long-term effects using an Autoregressive Distributed Lag (ARDL) model. The findings highlight significant future investment opportunities in non-financial assets, as well as in the lending and borrowing sectors. Additionally, the analysis of financial assets and liabilities reveals that while the incurrence of liabilities positively influences the gross operating balance, the acquisition of financial assets has a significant negative impact. Based on these findings, the study recommends that entrepreneurs and policymakers prioritise investments in non-financial assets and strategically manage liabilities to maximise economic opportunities. Furthermore, policymakers should introduce regulatory reforms to enhance the attractiveness of financial asset investments, ensuring a more positive contribution to the UAE’s economic sustainability. Future research should further investigate the underlying factors contributing to the negative impact of financial asset acquisitions on the gross operating balance. Additionally, further studies should identify the most promising non-financial asset investment opportunities to support sustainable entrepreneurial growth in the UAE.