Over the last decade, promotion of competitiveness represents one of the central goals of economic policy of most of the countries. Moreover, in recent years, the promotion of competitiveness has been seen as a way of achieving desirable changes in economy and society. While there is no unity of views in the theory regarding the conceptual definition of the phenomenon of competitiveness, it is becoming less arguable that in strictly economic terms, competitiveness is a synonym for productivity. However, it should be noted that productivity growth that is accompanied by increasing social imbalance (for example, inequality in income distribution), on the one hand, and environmental pollution, on the other hand, cannot be a guarantee of improving the competitiveness of countries in the long run. Acknowledging precisely this fact and using the data from World Economic Forum on Global Competitiveness 2013, this paper elaborates on the phenomenon of sustainable competitiveness and tests the hypothesis about the positive impact of its social and environmental dimension on the economic dimension of sustainable competitiveness that is represented by the value of the Global Competitiveness Index. The survey of 34 countries confirmed the indisputable positive impact of the social dimension of sustainability, but also variable direction of the impact of the environmental dimension of sustainability (depending on the level of GDP per capita) on the economic dimension of sustainable competitiveness of European countries in 2013.

This work presents a robust framework for quantifying solar irradiance variability and forecastability through the Stochastic Coefficient of Variation ( ) and the Forecastability ( ). Traditional metrics, such as the standard deviation, fail to isolate stochastic fluctuations from deterministic trends in solar irradiance. By considering clear-sky irradiance as a dynamic upper bound of measurement, provides a normalized, dimensionless measure of variability that theoretically ranges from 0 to 1. extends by integrating temporal dependencies via maximum autocorrelation, thus linking with . The proposed methodology is validated using synthetic cyclostationary time series and experimental data from 68 meteorological stations (in Spain). Our comparative analyses demonstrate that and proficiently encapsulate multi-scale fluctuations, while addressing significant limitations inherent in traditional metrics. This comprehensive framework enables a refined quantification of solar forecast uncertainty, supporting improved decision-making in flexibility procurement and operational strategies. By assessing variability and forecastability across multiple time scales, it enhances real-time monitoring capabilities and informs adaptive energy management approaches, such as dynamic outage management and risk-adjusted capacity allocation.