Artificial intelligence (AI) adoption in strategic sectors such as energy is often framed in optimistic narratives, yet its actual economic contribution remains under-quantified. This study proposes a novel, multi-stage methodology at the intersection of machine learning, statistics, and big data analytics to bridge this gap. First, we construct a media-derived AI Adoption Score using natural language processing (NLP) techniques, including dictionary-based keyword extraction, sentiment analysis, and zero-shot classification, applied to a large corpus of firm-related news and scientific publications. To enhance reliability, we introduce a Misinformation Bias Score (MBS)—developed via zero-shot classification and named entity recognition—to penalise speculative or biased reporting, yielding a credibility-adjusted adoption metric. Using these scores, we classify firms and apply a Fixed Effects Difference-in-Differences (FE DiD) econometric model to estimate the causal effect of AI adoption on turnover. Finally, we scale firm-level results to the macroeconomic level via a Leontief Input–Output model, quantifying direct, indirect, and induced contributions to GDP and employment. Results show that AI adoption in Romania’s energy sector accounts for up to 42.8% of adopter turnover, contributing 3.54% to national GDP in 2023 and yielding a net employment gain of over 65,000 jobs, despite direct labour displacement. By integrating machine learning-based text analytics, statistical causal inference, and big data-driven macroeconomic modelling, this study delivers a replicable framework for measuring credible AI adoption and its economy-wide impacts, offering valuable insights for policymakers and researchers in digital transformation, energy economics, and sustainable development.

This present work investigates the spatial distribution of the unemployment rate in Spain, one of the European countries with the highest recorded level of unemployment. The unemployment rate reached 16% during the pandemic. For this purpose, data from 2020 were used, such as the growth rate of the number of companies and entrepreneurs, the percentage of the workforce employed in the industrial sector, and the percentage of young people aged 16–25 years. All of these data were collected at the provincial level. The importance of spatiality in unemployment estimation is proven using regressions estimated in Geoda and GeodaSpace. The results support the introduction of the lag factor in regressions, improving the performance of the OLS model. However, the use of error models was found to be inefficient. Moreover, creating local estimates of coefficients can effectively adapt to the unique spatial characteristics of Spanish provinces. The research focuses on the sustainability challenges linked to regional inequalities in unemployment. It argues that these inequalities disrupt the balanced distribution of economic activities and hinder the achievement of long-term sustainable development across Spain’s regions. Resolving these inequalities is crucial for promoting regional competitiveness and overall economic growth.

Romania, along with many other nations, must take urgent action to significantly reduce greenhouse gas emissions to effectively address climate change. Through an examination of Romania’s renewable energy potential and strategies for transitioning to a more sustainable energy system, this study employs a Vector Autoregressive (VAR) model to explore the complex dynamic interrelationships between various energy sources within Romania including solar power, hydropower, coal, and natural gas. The results indicate that increased production of renewable resources, particularly solar and hydroelectric energy, serves to diminish reliance on fossil fuels by negatively impacting thermal energy output. Impulse response functions reveal that short-term shocks to thermal energy production lead to temporary decreases in solar and nuclear energy generation, followed by a gradual stabilisation, while variance decomposition analysis underscores solar energy's pivotal role in influencing other energy sources. The findings highlight Romania’s ability to replace polluting sources with environmentally friendly alternatives, implying that policies aimed at diversifying the energy mix, investing in green infrastructure development, and fostering innovation across industries are fundamental. Such strategies not only enhance Romania's energy sustainability over the long term but also contribute to broader economic growth and environmental protection objectives.

In the ever-evolving financial landscape, the integration of artificial intelligence (AI) and machine learning (ML) is revolutionising creditworthiness assessment. The vast body of literature on credit rating indicates a growing prevalence of these techniques in the rating processes. Although these methods boast high predictive accuracy, concerns about their robustness, equity, and explainability affect the confidence of various parties in rating agencies. This comprehensive study explores the dynamic intersection of these cutting-edge technologies with rating agencies, presenting an in-depth literature review employing a bibliometric analysis that uses the Bibliometrix and Biblioshiny packages from R. The paper makes a significant contribution by analysing the literature across three prominent databases: Web of Science, Scopus, and arXiv. The empirical findings indicate that despite a recent growing interest, the relatively limited number of documents implies that, while there is a wide literature about credit rating in general, when it comes to rating agencies, the literature is much more limited. This limitation may stem from a certain lack of transparency in the methods and processes used by rating agencies and the complex nature of these entities. The literature witnessed growth after the 2008 global financial crisis, where rating agencies faced significant criticism, and post-pandemic, indicating a need for more adaptable and precise ratings. The examination of the topic reveals a recent shift in focus within AI-driven rating agencies towards accountable governance. While traditional attention persists on artificial intelligence techniques and finance, the emerging emphasis on ethical considerations, societal impacts, and performance evaluation underscores a changing landscape. This transition underscores the growing importance of integrating ethical considerations and societal impacts into the operational frameworks of AI-powered rating agencies, emphasising the necessity for responsible and transparent decision-making practices.