Browsing by Author "Pirciog Camelia Speranta"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Demographic Dependency and the Future of the European Workforce: A Spatial–Temporal Forecasting Approach
    (MDPI, 2026-05-01) Lincaru, Cristina; Grigorescu Adriana; Pirciog Camelia Speranta; Tudose Gabriela
    This research paper examines the spatial and time variation of demographic dependency in Europe in a 30-year horizon of the evolution of the demographic dividend regarding the economic dependency ratio (ADR1). We used the Curve Fit Forecast tool to estimate the trends of ADR1 in each of the EU Member States using data on Eurostat projections and a sophisticated geostatistical analysis tool developed in ArcGIS Pro 3.2.2. The findings indicate that the dependency in all countries has increased significantly in a statistically significant manner as the Gompertz function has appeared as the best curve in a third of the cases. It is an S-shaped asymptotic behaviour of this function that effectively describes the nonlinear patterns of acceleration and saturation of demographic ageing. As indicated in the analysis, the European regions are increasingly moving apart, with the southern and eastern nations such as Romania demonstrating the most alarming decline in ADR1. These trends highlight the need to reform labour market policies and social protection mechanisms to an ageing population. The paper combines the curve-fitting, descriptive statistics (median, skewness, interquartile range (IQR)) with time clustering (value, correlation, and Fourier) to provide an effective, replicable approach to early warning and policy prioritisation. Overall, the results highlight the importance of integrating predictive spatial modelling and demographic economics to support anticipatory and evidence-based policy decisions. The proposed approach proves to be a robust and transferable framework, applicable to a wide range of socio-economic phenomena characterised by inertia and structural change. Future research should extend the analysis to subnational levels, incorporate additional explanatory variables, and develop scenario-based simulations, including multivariate Gompertz-type models, to further enhance both predictive accuracy and policy relevance in the context of emerging structural labour scarcity.
  • Thumbnail Image
    Item
    Solar energy production in EU + countries using space–time forecasting
    (Springer Nature, 2026-01-09) Grigorescu, Adriana; Lincaru, Cristina; Pirciog Camelia Speranta
    The need for renewable energy is underscored by the shift from fossil fuels without compromising economic growth. The European Union’s strategy for resilient energy recognizes solar energy as a crucial resource. In recent years, there has been a significant focus on evaluating renewable energy production, distribution, consumption, storage, and applications. One key aspect of this evaluation is exploring production capacity trends using forecasting models. Our study proposes a space–time forecasting approach using the Curve Fit Forecast tool integrated in ArcGIS Pro 3.3, applied to solar energy in 37 European locations based on 1990–2022 data. The model tested four curved types and identified exponential growth patterns in 54% of cases, with consistent upward trends confirmed through RMSE validation. Outlier detection and 3D spatial–temporal visualization provide diagnostic insight into regional disparities and transformation regimes. This digitally enabled, geostatistical method aligns with the EU’s data-driven energy transition agenda, offering a scalable and interpretable tool for policymakers and planners.