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The purpose of this paper is to assess the efficiency of climate change competitiveness via a case study on EU regions by using the data envelopment analysis (DEA) model and Shannon entropy. First, on the same premise as similar composite indicators, we develop a DEA model to assess the relative performance of the regions in climate change competitiveness. Then, we extend our calculations with a DEA-like model and Shannon entropy to derive global estimates of a new competitiveness index by using common weights. Results show that the proposed DEA-Entropy model enables the construction of a regional climate change competitiveness index among all regions via a set of common weights. The proposed model's common weight structure demonstrates more discriminative power compared to the weights obtained through pure DEA or DEA-like methods. In order to validate the proposed DEA-Entropy model, it was applied to 120 EU regions. The results are meaningful for the regions to improve their competitiveness. [ABSTRACT FROM AUTHOR]

Precipitation patterns are critical for understanding the hydrological and climatological dynamics of any region. Sicily, the largest island in the Mediterranean sea, with its diverse topography and climatic conditions, serves as an ideal case study for analyzing precipitation data, to gain insights into regional water resources, agricultural productivity, and climate change impacts. This paper employs advanced statistical physics methods, particularly Tsallis q-statistics, to analyze sub-hourly precipitation data from 2002 to 2023, provided by the Sicilian Agrometeorological Informative System (SIAS). We investigate several critical variables related to rainfall events, including duration, depth, maximum record, and inter-event time. The study spans two decades (2002–2012 and 2013–2023), analyzing the distributions of relevant variables. Additionally, we examine the simple returns of these variables to identify significant temporal changes, fitting these returns with q-Gaussian distributions. Our findings reveal the scale-invariant nature of precipitation events, the presence of long-range interactions, and memory effects, characteristic of complex environmental processes. [ABSTRACT FROM AUTHOR]