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Driving Sustainable Development from Fossil to Renewable: A Space–Time Analysis of Electricity Generation Across the EU-28
(MDPI, 2025-11-26) Grigorescu, Adriana; Lincaru, Cristina; Pirciog Camelia Speranta
The transition to renewable energy is crucial in order to attain sustainable development, lower greenhouse gas emissions, and secure long-term energy security. This study examines spatial–temporal trends in electricity generation (both renewable and non-renewable) across EU-28 countries using monthly Eurostat data (2008–2025) at the NUTS0 level. Two harmonized Space–Time Cubes (STCs) were constructed for renewable and non-renewable electricity covering the fully comparable 2017–2024 interval, while 2008–2016 data were used for descriptive validation, and 2025 data were used for one-step-ahead forecasting. In this paper, the authors present a novel multi-method approach to energy transition dynamics in Europe, integrating forecasting (ESF), hot-spot detection (EHSA), and clustering (TSC) with the help of a new spatial–temporal modeling framework. The methodology is a step forward in the development of methodological literature, since it regards predictive and exploratory GIS analytics as comparative energy transition evaluation. The paper uses Exponential Smoothing Forecast (ESF) and Emerging Hot Spot Analysis (EHSA) in a GIS-based analysis to uncover the dynamics in the region and the possible production pattern. The ESF also reported strong predictive performance in the form of the mean Root Mean Square Errors (RMSE) of renewable and non-renewable electricity generation of 422.5 GWh and 438.8 GWh, respectively. Of the EU-28 countries, seasonality was statistically significant in 78.6 per cent of locations that relied on hydropower, and 35.7 per cent of locations exhibited structural outliers associated with energy-transition asymmetries. EHSA identified short-lived localized spikes in renewable electricity production in a few Western and Northern European countries: Portugal, Spain, France, Denmark, and Sweden, termed as sporadic renewable hot spots. There were no cases of persistent or increase-based hot spots in any country; therefore, renewable growth is temporally and spatially inhomogeneous in the EU-28. In the case of non-renewable sources, a hot spot was evident in France, with an intermittent hot spot in Spain and sporadic increases over time, but otherwise, there was no statistically significant activity of hot or cold spots in the rest of Europe, indicating structural stagnation in the generation of fossil-based electricity. Time Series Clustering (TSC) determined 10 temporal clusters in the generation of renewable and non-renewable electricity. All renewable clusters were statistically significantly increasing (p < 0.001), with the most substantial increase in Cluster 4 (statistic = 9.95), observed in Poland, Finland, Portugal, and the Netherlands, indicating a transregional phase acceleration of renewable electricity production in northern, western, and eastern Europe. Conversely, all non-renewable clusters showed declining trends (p < 0.001), with Cluster 5 (statistic = −8.58) showing a concerted reduction in the use of fossil-based electricity, in line with EU decarbonization policies. The results contribute to an improved understanding of the spatial dynamics of the European energy transition and its potential to support energy security, reduce fossil fuel dependency, and foster balanced regional development. These insights are crucial to harmonize policy measures with the objectives of the European Green Deal and the United Nations Sustainable Development Goals (especially Goals 7, 11, and 13).
GENERATIVE AI AND LABOUR MARKET RESEARCH INTEREST FRAMEWORK
(De Gruyter Brill, 2024) Grigorescu, Adriana; Joița, Florina
This study investigates the intersection between generative artificial intelligence (GenAI) and labour market by developing a comprehensive framework to analyse the current state of scientific interest in this emerging topic. The research employs a quantitative methodology, using a comparatively implemented bibliometric analysis, thus examining two major databases, Web of Science and Scopus with the aim to provide a deeper understanding of academic landscape. The research focuses on the database with the largest number of relevant papers, providing insight into the concentration of academic activity in the field, both in terms of evolution over time, trends, countries, keywords and authors with the highest research impact. The research reveals a significant gap in the literature concerning the impact of GenAI in labour market, with only one small percentage of papers addressing this topic. Key findings include a rise in publications post-2018, particularly from the USA, Russia and China, and a lack of developed research networks. This article concludes the further exploration of the implication of GenAI on the labour market is needed, with potential directions for future research
Intersectoral Labour Mobility in Europe as a Driver of Resilience and Innovation: Evidence from Granularity and Spatio-Temporal Modelling
(MDPI, 2025-11-18) Lincaru, Cristina; Pirciog, Camelia Speranta; Grigorescu, Adriana; Mladen-Macovei, Luise
Intersectoral labour mobility is a key driver of economic resilience and innovation in Europe. The redistribution of workers across sectors and regions enables economies to adapt to shocks, create flexibility and increase the rate of structural change. However, the dynamics of mobility have not been adequately investigated across varying scales of sectoral granularity and spatio-temporal dimensions. This paper applies the Intersectoral Mobility Index (MI) to all European NUTS-2 areas from 2008 to 2020, utilising Eurostat Structural Business Statistics. Two levels of sectoral aggregation (NACE Rev. 2, 1-digit and 2-digit) are employed to compute MI, capturing both broad and fine-grained reallocations. Classical indices of structural change (NAV, Krugman, Shorrocks) are combined with spatio-temporal modelling in ArcGIS Pro, employing Space–Time Cubes, time-series exponential smoothing forecasts, time-series clustering and emerging hot spot analysis. Results indicate that MI distributions are positively skewed and heavy-tailed, with peaks coinciding with systemic crises (2009–2011, 2020). At the 2-digit level, MI values are significantly higher, revealing intra-sectoral changes obscured in aggregated data. A statistically significant downward trend in mobility suggests an increasing structural rigidity following the global financial crisis. Regional clustering highlights heterogeneity: a small number of regions, such as Bremen, Madeira and the Southern Great Plain, have sustained high or unstable mobility, while most exhibit convergent mobility and low reallocation. This paper contributes to the conceptualisation of MI as a dual measure of resilience and innovation preparedness. It underscores the importance of multi-scalar and spatio-temporal methods in monitoring labour market flexibility. The findings have policy implications, including the design of targeted reskilling programmes, proactive labour market policies and just transition plans to maintain regional resilience during the EU’s green and digital transitions.
Quality of Life Determinants in Spain's Smart Rural Areas During the Pandemic: A Better Alternative to Urban Living
(MDPI, 2025) López-Ruiz, Victor-Raul; Alfaro-Navarro, Jose-Luis; Nevado-Peña, Domingo; Huete-Alcocer, Nuria; Grigorescu, Adriana
In the context of widespread rural depopulation, recent global developments have sparked shifts in lifestyle preferences, underscoring the often-overlooked benefits of rural living. These changes have prompted many people to consider relocating to rural areas for improved quality of life and more tranquility. This study explores the differences in quality of life between rural and urban environments, analyzing key factors that impact individuals' perceived well-being within a broader societal context. Focusing on Spain, our research aims to identify the diverse elements influencing social happiness. Using data from a national survey on quality of life for 908 participants, we compared the perspectives of rural and urban residents during the pandemic, revealing a clear attraction effect toward rural areas. Our findings reveal a significant trend toward relocating to rural areas, but only those with good connectivity (smart rural areas). To effectively address rural depopulation and promote these regions, it is crucial to invest in comprehensive connectivity enhancements, including technological advancements, improved infrastructure, and wider availability of services. Methodologically, a comparative analysis approach was applied, using an analysis of variance (ANOVA) to identify differences and regression techniques to determine the influence of various factors on habitat happiness, understood as quality of life in social terms.

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