This study aims to explore the relationship between industry value added, renewable energy, and CO₂ emissions in a sample of 44 Sub-Saharan African countries over the period 2000–2015. This study makes several important contributions to extant research. While existing research was focused on the renewable energy-CO₂ emissions nexus, the current study assesses the moderating role of the renewables sector in the industrialization-CO2 emissions relationship. In addition, this study considers whether EKC relationships will hold after accounting for structural transformations (including industrial contributions to GDPs). Moreover, we are revising the existence of the EKC framework for the Sub-Saharan African countries. Using a two-step system GMM estimator, we found that the share of industry in GDP has a significant positive impact on CO₂ emissions, while renewable electricity output reduces CO₂ emissions. If causal, a one percentage point increase in renewable electricity output reduces carbon emissions by 0.22%. Moreover, the renewable energy sector then mediates the positive effect of industry value added on CO₂ emissions. We also find evidence for the statistical significance of the inverted U-shaped relationship between GDP per capita and CO₂ emissions.

The widespread use of information and communication technologies and subsequent transformations have led to the formation of a digital economy (DE). The European Union, as an international organization, has become the subject of building such an economy, striving to bring member countries closer in the field of digitalization.The aim of this paper is to compare the DE development parameters of the EU countries based on cluster analysis and determine the most significant of them to solve the problems of bridging the digital divide between countries. For clustering, a feature DE vector of 20 indicators was created and the k-means algorithm and the Euclidean distance metric were used. For classification, the decision tree method was applied.Three clusters of EU countries were identified by the level of DE development (leaders, followers and outsiders), which allowed assessing their positions relative to each other. Key parameters that determine countries’ positions in the general rating are identified. A parameter chart is generated to control the establishment of DE in the EU countries, which, in addition to key parameters, includes maximum, minimum and harmonic mean values of these parameters by cluster. This characterizes the landscape of DE development in the EU countries, assesses the digital divide and is the basis for decision-making in the area of bridging this divide.

Growing environmental pressures and climate‐related risks have intensified concerns regarding population health and sustainability. This study explores how the predictive relationship between public health and key sustainability indicators, such as agricultural emissions, forests, governance, digitalization, green energy, and climate outcomes, varies over time across the United States and China. For this, the study applied the Bootstrap rolling window Granger causality framework to identify whether health sustainability relationships remain stable or vary over time. The study uses the annual data from 1996 to 2023. The findings revealed different structural patterns. For instance, in the United States, causal flows from sustainability factors to health with episodic positive effects from forest, green energy, and climate outcomes, while digitalization exhibited weak health reinforcement. In contrast, for China, health exhibits stronger predictive effects on agricultural emissions, renewable energy transitions, and governance indicators. The results indicate distinct sustainability health pathways, with environmental conditions playing a more prominent role in health dynamics in the United States, while improvements in health are more closely associated with sustainability transition in China, which can be used to inform resilience‐oriented policy development.