The paper analyses the linkages between GDP per capita, greenhouse gas (GHG) emissions, and renewable energy (RE) in the total final energy consumption and green investments (PICE) which are measured as private investments, jobs, and gross value added related to circular economy sectors. The object of the analysis is the EU countries during the 2008-2016 period (crisis and post-crisis period). In the paper, data from the following databases was used: the Eurostat, the World Data Bank, and the European Environmental Agency. For addressing the linkages between the aforementioned indicators, the following methods were applied: panel unit root test, Pedroni panel cointegration tests, and the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) panel cointegration techniques. The findings show that FMOLS and DOLS demonstrate the same results as GHG, PICE, RE influence on GDP of the EU countries. The findings prove there is linking between gross domestic product per capita, greenhouse gas emissions, renewable energy in the total final energy consumption and green investments. The findings also show that green investment (PICE) could provoke the growth of GDP per capita by 6.4%, the decline of GHG by 3.08%, and the increase of renewable energy in the total final energy consumption by 5.6%.

In practice, there is a massive time lag between data loss and its cause identification. The existing techniques perform it comprehensively, but they consume too much time, so there is a need for fast and reliable methods. The article’s purpose is to develop a rapid methodology to assess the risk of information and knowledge loss management. It provides the implementation of eight steps and combines a risk mapping method modified by assessments based on risk factors and incidents as elements from set theory and using formalization via binary estimates. The methodology includes five significant events caused by the company staff, technical problems, software, cybercriminals, viral attacks, and 66 factors influencing company incidents. As a result, a risk map of 9 groups was built for a Ukrainian enterprise. Only two groups with the minimum number of incidents and low losses are represented by all five incidents. The defined overall level of each risk group ranges from 0.14 to 0.26, which indicates a low probability of all happenings in the group. In general, the resulting map shows the existence of specific security problems of the company under investigation. The proposed assessment allows us to interpret the level of risk in the company quickly, identify weaknesses in the information security system, and predict future losses.

A smart city is a complex concept that can be analyzed from different aspects and points of view. E-governance plays a key role in facilitating the integration of all elements of a smart city. The purpose of the article is to investigate key enablers of e-governance in terms of economic, social, political, information and technological indicators. The research base includes 68 smart cities selected on the basis of different regional affiliations and different economic, social and political developments. The authors apply the methods of cluster analysis (to divide smart cities into clusters according to e-governance indicators); construction of an integral indicator using the linear mathematical model and the Fishburn formula; VAR/VEC modeling (to stud the key factors influencing the development of e-government in smart cities). It was found that the Human Development Index has the greatest impact on e-governance, while the GNI per capita indicator demonstrated the absence of influence for all clusters. The factor of information technologies was defined as the main factor of direct influence on the Smart City Governance Index for smart cities of the first cluster with the highest indicators of e-governance.

The development of digital technologies is one of the factors influencing the cities’ readiness for the COVID-19 breakout. The purpose of this article is to assess cities’ resilience to the COVID-19 pandemic depending on the “smart” level criteria. The article uses the following research methods: (1) bibliometric analysis to identify the main directions of scientific research regarding “COVID-19” and “smart city” in Scopus publications for 2019–2022; (2) k-means clustering method to identify common patterns among smart cities regarding their readiness and responsiveness to COVID-19; (3) correlation analysis to identify the relationships between smart city performance indicators and COVID-19 severity in these cities. The Smart City Index 2021 was a key criterion for classifying a city as smart for this study. The correlation analysis included two stages: (1) correlation analysis of the Smart City Rank and indicators of COVID-19 readiness and responsiveness; (2) correlation analysis of the Smart City Rank and its health care components and COVID-19 severity indicators. According to the study results, smart cities demonstrated higher COVID-19 readiness and lower COVID-19 fatality rates. However, they lag behind in terms of resilience and sustainability of their health care systems.