Sustainable development as a concept, is extremely important both at national and international levels. To achieve the goals of sustainable development, international cooperation among countries is of vital importance because no one nation can accomplish these goals independently. In order to implement the sustainable development strategy, first discussed in 1992 at the UN Conference of Environment and Development, the indicators of sustainable development first had to be defined. Considering that the first set of indicators was defined by the Commission on Sustainable Development (CSD), they were entitled CSD indicators. The smaller set of CSD indicators in the European Union countries (EU-28) is observed in this study. These indicators cover three aspects of sustainable development: economic, social and environmental. The goal of this study is to rank EU countries according to the level that they were able to achieve the objectives of their respective sustainable development strategies individually, according to the 2013 database. The appropriate statistical I-distance method was used for this purpose. In the first step, the significant indicators are distinguished and ranked according to the quantity and importance of the information they provide for specific research. Subsequently, this method then converts the indicators into a single measure that reflects the level at which each country has achieved the goal.

We consider the problem of testing independence in mixed-type data that combine count variables with positive, absolutely continuous variables. We first introduce two distinct classes of test statistics in the bivariate setting, designed to test independence between the components of a bivariate mixed-type vector. These statistics are then extended to the multivariate context to accommodate: (i) testing independence between vectors of different types and possibly different dimensions, and (ii) testing total independence among all components of vectors with different types. The construction is based on the recently introduced Baringhaus-Gaigall transformation, which characterizes the joint distribution of such data. We establish the asymptotic properties of the resulting tests and, through an extensive power study, demonstrate that the proposed approach is both competitive and flexible.