The environmental Kuznets curve (EKC) hypothesis posits an inverted U-shaped relationship between economic growth and environmental degradation. However, there is no consensus regarding the EKC hypothesis among countries and regions of different income groups. This study revisits the EKC hypothesis by employing cross-correlation analysis to explore the income–CO₂ emissions relationship across 158 countries and 44 regions from 1990 to 2020. The empirical method utilizes a dynamic cross-correlation coefficient (CCC) approach, allowing for the assessment of lead-lag dynamics between income and CO₂ emissions over time. By categorizing nations into the World Bank’s income classifications, we found a heterogeneous EKC pattern highlighting distinct environmental–economic dynamics across different income groups. The findings indicate that high-income countries show a decoupling of economic growth from CO₂ emissions; whereas, low-income countries still exhibit a positive correlation between both variables. This underscores the necessity for tailored policy interventions that promote carbon neutrality, while considering each country’s unique development stage. Our research contributes to the ongoing issue of sustainable economic development by providing empirical evidence of the different pathways nations follow in balancing growth with environmental preservation.

Cryptocurrency investments are often perceived as uncertain and risky. In this study, we assessed if this is indeed the case, using a sample of seven cryptocurrencies and considered a period that encompassed the first real global shock in the life of these relatively new financial assets, the COVID-19 pandemic. Uncertainty was evaluated using Shannon’s symbolic entropy. To measure risk, we use value-at-risk and conditional value-at-risk. The results indicate that, except for Tether, the analyzed cryptocurrencies’ returns exhibited similar patterns of uncertainty and risk. Levels of uncertainty were close to the maximum values, but high uncertainty is not always associated with high risk. During the pandemic crisis, uncertainty increased while risk decreased, suggesting that the considered assets may have safe haven properties.

The relationship between education and societal development is unquestionable. Education contributes to achieving both societies’ and individuals’ social and economic goals. Quality education is recognized as one of the Sustainable Development Goals (SDGs), which, jointly with other behaviors and attitudes, could impact the development of societies in other fields like health and well-being, cultural preservation, environmental sustainability, and even peace and stability—all of them also listed as SDGs. However, the capacity, or not, to reach higher levels of compliance with quality in education (SDG 4) varies from country to country, according to the 2023 Sustainable Development Report results. Thus, the present study aims to identify the sufficient conditions for achieving higher levels of quality education (SDG 4) globally and to analyze how these conditions vary across different world regions. Applying a fuzzy set qualitative comparative analysis and using data from the 2023 Sustainable Development Report, we focus our analysis on four SDG 4 indicators—early education, primary education, lower secondary education, and literacy rate—across 117 countries, in order to assess the conditions for attaining higher levels of quality education. The results reveal there are specific and identifiable conditions that are sufficient for achieving higher levels of quality education on a global scale, with significant regional variations. These insights contribute to understanding the complex dynamics of educational quality and could be used as guidance for policymakers and educators aiming to improve educational outcomes worldwide.

In this research work, we propose to assess the dynamic correlation between different mobility indices, measured on a daily basis, and the new cases of COVID-19 in the different Portuguese districts. The analysis is based on global correlation measures, which capture linear and non-linear relationships in time series, in a robust and dynamic way, in a period without significant changes of non-pharmacological measures. The results show that mobility in retail and recreation, grocery and pharmacy, and public transport shows a higher correlation with new COVID-19 cases than mobility in parks, workplaces or residences. It should also be noted that this relationship is lower in districts with lower population density, which leads to the need for differentiated confinement policies in order to minimize the impacts of a terrible economic and social crisis.

Cryptocurrencies are relatively new and innovative financial assets. They are a topic of interest to investors and academics due to their distinctive features. Whether financial or not, extraordinary events are one of the biggest challenges facing financial markets. The onset of the COVID-19 pandemic crisis, considered by some authors a “black swan”, is one of these events. In this study, we assess integration and contagion in the cryptocurrency market in the COVID-19 pandemic context, using two entropy-based measures: mutual information and transfer entropy. Both methodologies reveal that cryptocurrencies exhibit mixed levels of integration before and after the onset of the pandemic. Cryptocurrencies displaying higher integration before the event experienced a decline in such link after the world became aware of the first cases of pneumonia in Wuhan city. In what concerns contagion, mutual information provided evidence of its presence solely for the Huobi Token, and the transfer entropy analysis pointed out Tether and Huobi Token as its main source. As both analyses indicate no contagion from the pandemic turmoil to these financial assets, cryptocurrencies may be good investment options in case of real global shocks, such as the one provoked by the COVID-19 outbreak.