The paper aims to analyze the contagion effect coming from the developed stock markets of the US and Germany to the emerging CEE stock markets of Romania, Czech Republic, Hungary, and Poland using daily data for the period April 2005–April 2021. The paper also captures the level of integration of these emerging stock markets by analyzing the volatility spillover phenomenon. The quantification of the contagion effect coming from the developed to the emerging stock markets consisted of an empirical analysis based on the DCC-GARCH (Dynamic Conditional Correlation) model. Through this multivariate model, the time-varying conditional correlations were analyzed, both in periods of normal economic development and in times of economic instability, when there was a significant increase in the correlation coefficients between developed and emerging stock market indices. Furthermore, the level of connectedness between these markets has been analyzed using the volatility spillover index developed by Diebold and Yilmaz. The empirical results surprised the high level of integration of the analyzed stock markets in Central and Eastern Europe, with the intensity of volatility transmission between these markets increasing significantly during times of crisis. All stock market indices analyzed show periods during which they transmit net volatility and periods during which they receive net volatility, indicating a bidirectional volatility spillover phenomenon. Mostly, the BET, PX, and WIG indices are net transmitters of volatilities, whereas the BUX index is net recipient, except during the COVID-19 crisis, when it transmitted net volatility to the other three indices. Finally, using a Markov switching-regime VAR approach with two regimes, we explored the contagion effect between emerging CEE and developed stock markets during the COVID-19 pandemic. The empirical results proved a shift around the outbreak of the health crisis, after which the high volatility regime dominates the CEE markets. The contagion effects from developed stock markets to emerging CEE markets significantly increased during the first stage of the health crisis.

Biopharmaceutical companies are critical in developing vaccines, treatments, and diagnostics for COVID-19. Thus, understanding the contagion effects of their stock market can have important economic implications, especially in the context of global financial markets. Due to the COVID-19 pandemic, biopharmaceutical companies’ stock markets may have experienced sudden volatility and risk changes, which may have had spillover effects on other sectors and markets. Policymakers can take pre-emptive measures to stabilize financial markets. Analyzing the contagion effects makes it even more relevant to analyze the stock market response of four leading pharmaceutical companies that either developed vaccines against COVID-19 or drugs that help to fight the virus, namely, Pfizer, AbbVie Inc., Sanofi, and Bristol Myers Squibb. The analysis considers two periods, before and during the COVID-19 crisis, and considers the influence of the market volatility and technological market index. In order to capture the contagion effects, DCC-GARCH models have been applied, which estimate time-varying correlation coefficients using a multivariate GARCH framework, allowing for the modeling of time-varying volatility and correlations in financial returns. The results reveal the impact of market volatility on the returns of all four pharmaceutical companies. Additionally, a contagion effect between all four companies, the technological market, and market volatility was observed during the COVID-19 period.

This study examines the validity of Okun’s law in Romania over the period 1994–2024, with a particular focus on the sustainability and inclusiveness of economic growth. The empirical analysis uses annual data from the World Bank’s World Development Indicators and applies the differential form of Okun’s law, relating changes in the unemployment rate to real GDP growth. A baseline specification is estimated alongside an extended model that includes investment and inflation as macroeconomic controls. The results indicate a negative but weak relationship between economic growth and changes in the unemployment rate. Across all model specifications, the estimated Okun coefficients are small and statistically insignificant, suggesting limited labor market responsiveness to output growth. Including investment and inflation modestly improves the explanatory power of the model but does not fundamentally alter the results. Overall, the findings point to the prevalence of jobless growth, where economic expansion has not translated into substantial employment gains. The evidence suggests that economic growth in Romania over the past three decades has had a limited capacity to reduce unemployment. From a sustainability perspective, these findings highlight the importance of complementing growth-oriented strategies with structural and labor market policies aimed at strengthening the employment-generating potential of economic growth and supporting inclusive and sustainable development.

Uranium mine closure represents a complex, multi-stage process encompassing technical decommissioning, environmental remediation, and socio-economic adjustment, with impacts extending beyond the mining sector. In countries operating nuclear power plants, such as Romania, the closure of domestic uranium mining activities also affects regional labour markets and long-term energy system configuration. This study assesses the medium- and long-term impacts of uranium mine closure from a process-oriented perspective, focusing on environmental cost internalization, workforce transition, and institutional coordination. The research adopts a mixed-method approach combining documentary analysis of regulatory and strategic frameworks with a quantitative survey conducted among 50 experts in mining engineering, environmental management, and energy-related fields. Descriptive statistical techniques are used to evaluate expert perceptions regarding the effectiveness and coherence of mine closure processes, including environmental protection measures, social compensation mechanisms, and post-closure development strategies. The results show that technical closure and environmental remediation measures are partially implemented and generally aligned with regulatory requirements. However, significant shortcomings persist in labour reconversion programs, inter-institutional coordination, and the integration of mine closure with regional development and renewable energy initiatives.

This study investigates how firms in high-emission sectors progress along the low-carbon transition by analysing the joint dynamics of Management Quality (MQ) and Carbon Performance (CP) using probabilistic modelling and explainable machine-learning methods. Digitalisation is conceptualised as the increasing use of data-driven and algorithmic tools in corporate governance, sustainability monitoring, and regulatory oversight, enabling a more granular assessment of corporate transition pathways across multiple time horizons. Using annual Transition Pathway Initiative data for 175 firms over the period 2018–2025, we apply discrete-time Markov chains to capture state persistence and directional mobility in MQ and CP, while Hidden Markov Models uncover latent performance regimes shaping firms’ transition trajectories across three decarbonisation horizons (2028, 2035, and 2050). To enhance interpretability and policy relevance, CatBoost-based feature importance analysis identifies governance, emissions-related, and sector-specific drivers of transitions between states. The results indicate a steady and highly persistent improvement in Management Quality, reflecting cumulative consolidation of governance structures, while Carbon Performance evolves more slowly and heterogeneously, with only moderate convergence emerging toward the 2050 horizon. Latent-regime estimates reveal a gradual shift from volatile, low-performance pathways toward more stable transition regimes over time. From a policy perspective, the findings suggest that governance improvements alone are insufficient to ensure timely emission reductions, highlighting the need for digitally enabled, sector-specific regulatory incentives and enforcement mechanisms targeting realised Carbon Performance.