Search

The paper aims to improve the methodology of the Digital Economic and Society Index (DESI), the European Commission's newest index to assess the development of the digital economy. In particular, we investigate whether methodological changes to the structure of DESI improve its ability to capture the digital transformation of EU economies. Using the sensitivity-based analysis, we check whether the selection of weights of individual elements included in the DESI is optimal or should be improved. We also verify the importance of DESI in explaining changes in GDP per capita in EU economies. In the literature, we find that digital transformation has enabled the creation of new business models and maximized efficiency in traditional firms. Using DESI, we empirically test whether the gap between rich and poor countries in European Union can be closed or eliminated through rapid and intensive digital transformation. Our results show that the DESI -- when modified by eliminating the pillars on internet services and digital public services -- has the same explanatory power. Connectivity is the dimension with the largest impact on digital transformation in EU countries. We also find that DESI is a significant regressor to explain changes in GDP per capita in EU countries. [ABSTRACT FROM AUTHOR]

The Global Competitiveness Index (GCI) developed by the World Economic Forum (WEF) is used as a standard for measuring a country's competitiveness. However, in literature, the GCI has been accused of numerous methodological flaws. Consequently, in 2018, the WEF introduced significant methodological changes. This study aims to examine whether the methodological modifications in the GCI's structure increase its ability to capture the real competitiveness of economies. In addition, the study considers whether the selection of weights of individual elements included in the GCI is optimal or could be improved. By employing a sensitivity-based analysis, we find that the change in methodology resulted in fewer pillars of marginal importance. In the case of the GCI 2017, there were four pillars, whereas in that of the GCI 4.0, there were only two pillars: product market and labor market. Furthermore, we reveal that the WEF weights do not reflect the measured importance of the variables. In the optimization process, numerous variables (primarily opinion-based indicators) were insignificant in explaining the GCI variance and could be eliminated from the set of diagnostic variables without affecting the index's value. For instance, in the case of the GCI 4.0, 35 out of 103 variables could be eliminated. The new rankings obtained by weight optimization and reduction of the diagnostic variables demonstrated a strong positive correlation with the original rankings. This research contributes to the literature from both a theoretical perspective (indicating the most vital indicators in the GCI) and a practical standpoint (reducing the costs and time of obtaining redundant data). [ABSTRACT FROM AUTHOR]

Research background: Composite indicators are commonly used as an approximation tool to measure economic development, the standard of living, competitiveness, fairness, effectiveness, and many others being willingly implemented into many different research disciplines. However, it seems that in most cases, the variable weighting procedure is avoided or erroneous since, in most cases, the so-called 'weights by belief' are applied. As research show, it can be frequently observed that weights do not equal importance in composite indicators. As a result, biased rankings or grouping of objects are obtained. Purpose of the article: The primary purpose of this article is to optimise and improve the Human Development Index, which is the most commonly used composite indicator to rank countries in terms of their socio-economic development. The optimisation will be done by re-scaling the current weights, so they will express the real impact of every single component taken into consideration during HDI's calculation process. Methods: In order to achieve the purpose mentioned above, the sensitivity analysis tools (mainly the first-order sensitivity index) were used to determine the appropriate weights in the Human Development Index. In the HDI's resilience evaluation process, the Monte Carlo simulations and full-Bayesian Gaussian processes were applied. Based on the adjusted weights, a new ranking of countries was established and compiled with the initial ranking using, among others, Kendall tau correlation coefficient. Findings & Value added: Based on the data published by UNDP for 2017, it has been shown that the Human Development Index is built incorrectly by putting equal weights for all of its components. The weights proposed by the sensitivity analysis better reflect the actual contribution of individual factors to HDI variability. Re-scaled Human Development Index constructed based on proposed weights allow for better differentiation of countries due to their socio-economic development. [ABSTRACT FROM AUTHOR]

• Sensitivity analysis is used to ensure equal importance of the variables. • In the EU, synergies among SDGs (41) are more common than trade-offs (2). • SDG5 – gender equality – has the highest associations number (all positive). • SDG7 – affordable and clean energy – is not significantly related to the others. • More developed economies lead in SDGs execution (Sweden, Denmark, the Netherlands). Sustainable development is one of the most urgent challenges facing humanity. Its basic principle is to improve people's well-being and maintain it over time. In 2015, the United Nations approved 17 sustainable development goals (SDGs) to ensure a better and sustainable future for all, balancing economic, social and environmental development. SDGs create an 'indivisible whole'; thus, examining their interactions is crucial. Our goals were twofold: (i) to assess the implementation degree of SDGs in EU countries and (ii) to examine the interactions between goals. The potential to achieve a given SDG is approximated by a composite indicator, calculated based on an innovative method of implementing tools derived from sensitivity analysis (SA). Respecting the degree of variability of individual variables and their correlation, we set the weights to equalise their importance. Moreover, the application of SA allows us to remove strongly correlated variables that are not transferring supplementary information. We use countries' linear ordering and Spearman's rank correlation coefficient to assess interactions between SDGs. Our research shows that Scandinavian countries predominately occupy the leading positions, respectively, eight times on the podium for Sweden and four by Denmark (of which three as a leader). The Netherlands also stands out, occupying the superior position in the performance of SDGs 2, 3, 5, 8, 9, 12 and 17. The top in achieving one of the most priority area – no poverty (SDG1) belongs to Czechia. Our study confirmed the results obtained by many researchers regarding the more potent synergy between SDGs compared to the trade-offs. The only trade-offs observed in our study relate to SDG15, which negatively correlated with SDG3 and 17. Furthermore, SDG7 was the only one not associated with others. [ABSTRACT FROM AUTHOR]

This article aims to improve one of the newest energy transition measures—the World Economic Forum WEF Energy Transition Index (ETI) and find its driving forces. This paper proposes a new approach to correct the ETI structure, i.e., sensitivity analysis, which allows assessing the accuracy of variable weights. Moreover, the novelty of the paper is the use the spatial error models to estimate determinants of the energy transition on different continents. The results show that ETI is unbalanced and includes many variables of marginal importance for the shape of the final ranking. The variables with the highest weights in ETI did not turn out to be its most important determinants, which means that they differentiate the analysed countries well; nonetheless, they do not have sufficient properties of approximating the values of the ETI components. The most important components of ETI (with the highest information load) belong to the CO2 emissions per capita, the innovative business environment, household electricity prices, or renewable capacity buildout. Moreover, we identified the clustering of both ETI and its two main pillars in Europe, which is not observed in America and Asia. The identified positive spatial effects showing that European countries need much deeper cooperation to reach a successful energy transition. [ABSTRACT FROM AUTHOR]

The last half a century has seen spectacular progresses in computing and modelling in a variety of fields, applications, and methodologies. Over the same period, a cross-disciplinary field known as sensitivity analysis has been making its first steps, evolving from the design of experiments for laboratory or field studies, also called 'in-vivo', to the so-called experiments 'in-silico'. Some disciplines were quick to realize the importance of sensitivity analysis, whereas others are still lagging behind. Major tensions within the evolution of this discipline arise from the interplay between local vs global perspectives in the analysis as well as the juxtaposition of the mathematical complexification and the desire for practical applicability. In this work, we retrace these main steps with some attention to the methods and through a bibliometric survey to assess the accomplishments of sensitivity analysis and to identify the potential for its future advancement with a focus on relevant disciplines, such as the environmental field. • The manuscript offers a detailed timeline of sensitivity analysis, tracing its evolution from local to global methods. • The manuscript includes a bibliometric survey assessing sensitivity analysis achievements and identifying future directions and areas of growth in the field. • We believe our research significantly contributes to the academic discourse on sensitivity analysis, aligning well with the focus of your journal. [ABSTRACT FROM AUTHOR]