Increases in productivity and competitiveness of an economy are based mostly on the actions of companies in terms of providing technical capital to workers and of operations efficiency (management, financial, recruitment, finding markets and suppliers, internal and external communication, etc.) through digitalization. This paper deals with the way in which the economies and companies in Central and Eastern European (CEE) countries manage to adapt to the trend that started, mostly after 2000, in digitalization; also, it analyzes the extent to which an increase in the degree of business upgrading via integrating digital technology into the business model leads to a surge in economic performances (productivity, exports) and, consequently, to a greater attractivity to foreign capital flows.

The aim of our study was to analyze whether the increase in the use of renewable energy can help GDP growth. The research carried out shows that renewable energy has the ability to decrease or neutralize the negative impact of greenhouse gases (GHG), but also to maintain economic growth. We focused our analysis on the EU-28 as we know that the EU Commission’s aim, in the near future, is to join forces to reduce the GHG used and move to renewable sources. We used a panel analysis with data between 2000 and 2019 from all Member States, and our results showed that their economic growth is influenced positively by the production of renewable energy, the GHG per capita, and the GHG intensity per GDP.

This study examines the impact of artificial intelligence (AI) on workforce skill requirements as AI becomes increasingly integrated into business operations. Using foresight analysis and scenario-based methods, we anticipate the necessary skills for future AI-integrated workplaces. A SWOT analysis evaluates three potential paths for AI adoption—gradual, aggressive, and selective—to project the evolving skills needed for employee success in changing business environments. The findings emphasize the critical need for both enhanced technical proficiency and soft skills, such as creative problem-solving and interpersonal abilities, across all AI adoption scenarios. The study highlights the importance of strategic reskilling and continuous learning to align employee skills with the new business paradigms shaped by AI. It provides a roadmap for businesses, educators, and policymakers to collaboratively develop a resilient and adaptable workforce for an AI-enhanced future.

The International Accounting Standards Board (IASB) and the International Sustainability Standards Board (ISSB) of the IFRS Foundation support the integrated reporting of companies’ financial and sustainability performance to stakeholders. This paper aims to investigate whether financial and environmental, social, and corporate governance (ESG) practices have a real impact on the success of the companies in the European renewable energy equipment and services industry. Using the Granger test, the causality between the market capitalization and financial indicators was established, whereas no causality was identified between the market capitalization and ESG performance. The research led to the conclusion that the investment decision is mainly based on the information provided by the financial statements of the companies, the early stages of sustainability reporting regulation, and the need for increasing the quality and availability of corporate social responsibility information for investors.

This paper investigates the effectiveness of machine learning algorithms in enhancing the accuracy and reliability of predicting financial distress. The dataset includes Altman Z-Scores and Corporate Governance Compliance (CGC) indicators calculated for manufacturing firms listed on the Bucharest Stock Exchange (BSE) from 2016 to 2022. Leveraging Signaling Theory, the study analyzes financial and governance data for 60 non-financial firms, comprising 420 firm-year observations. Financial distress is classified into three categories: no distress, moderate distress, and severe distress. The study employs a Random Forest classification model, leveraging artificial intelligence techniques to identify critical predictive variables and evaluate their combined effectiveness in signaling financial distress. The findings reveal that machine learning algorithms significantly improve the predictive accuracy and reliability of financial distress classifications, effectively distinguishing between different distress levels by integrating financial ratios and corporate governance variables. These results emphasize the advantages of involving artificial intelligence and advanced analytics in financial distress prediction models, enhancing transparency and strengthening investor confidence. The research contributes to the literature on digital transformation in financial analysis and corporate governance, offering practical implications for investors, managers, creditors, and policymakers in emerging market environments.