The article aims to analyze the impact of green investments and the development of renewable energy on greenhouse gas emissions based on 223 countries in 2011–2021. The information base is the International Renewable Energy Agency, Our World in Data, Climate Policy Initiative, and FTSE Russell. Correlation analysis was used to check the data multicollinearity, multivariate regression analysis with stepwise variable entry—to formalize functional relationships. All variables characterizing the dynamics of green investments and the development of alternative energy, the number of annual investments in off-grid renewable energy has the largest impact on the amount of CO₂ and N₂O. Thus, an annual investment increase of USD 1 million leads to a CO₂ emission increase of 4.5 kt and an N₂O emission increase of 0.272 kt. Simultaneously, the green economy’s market capitalization level has the largest impact on the amount of CH₄. In this case, a capitalization increases of USD 1 trillion leads to a CH₄ emission increase of 129.53 kt. The dynamics of renewable energy development have a statistically significant effect on only one of the three studied greenhouse gases—CO₂ emissions. Here, 1 MW growth of an absolute increase in off-grid renewable energy capacity leads to a 1171.17 kt reduction of CO₂ emissions. Checking input data for lags confirmed a time lag of one year between the level of green investments and the level of greenhouse gas emissions. That is, the impact of green investments on the level of greenhouse gas emissions is delayed by one year. The results of regression models taking into account lags confirmed that an increase in the level of green investments has a positive effect on reducing the level of greenhouse gas emissions (an increase in off-grid renewable energy annual investments of USD 1 million leads to a decrease in CO₂ of 1.18 kt and N₂O of 1.102 kt; the increase in green economy market capitalization of USD 1 trillion leads to a decrease in CH₄ emissions of 0.64 kt).

This work summarizes the arguments and counterarguments in the framework of the scientific debate on the issue of financial literacy. The main purpose of the research is to identify the key factors and behavioral patterns inherent in managing personal finances. Systematization of literary sources and approaches to solving the problem of insufficient level of financial literacy among different segments of the population indicates that it is necessary to promote financial education of the population, especially vulnerable segments. This concerns the issues of financial inclusion, improvement of financial education, and development of practical skills for making financial decisions. The urgency of solving this scientific problem is caused by the rapid development of financial services. The study of the problems of the theoretical foundations of financial literacy in the work is carried out in the following logical sequence: analysis of the publications, analysis of available databases, and statistical analysis. The methodological tools of the research were the Python programming language, in particular the stats model’s library. The object of the research is patterns of behavior with personal finances. The article presents the results of empirical statistical analysis, which showed that the difference in financial decisions of individuals is due to age, level of education, employment, and level of family income. Financial decisions such as saving or borrowing have been found to be related to financial literacy and influence financial confidence. The study empirically confirms and theoretically proves that financial literacy is a fundamental factor in the level of financial well-being and closely correlates with financial behavior patterns. The results of the research can be useful for further scientific developments.

The most significant problems in financing the public health system in Ukraine are the permanent deficit of public spending on medicine and the shallow development of the voluntary health insurance market. The aim of study is the search of optimal interactions between stakeholders in the system of relations “state – insurance companies – households” in the context of voluntary health insurance. The study hypothesizes that households can become more active participants in health insurance only if their average monthly income reaches a certain threshold level. It is calculated based on the results of simulation games using the Brown-Robinson iterative method. According to the simulation results, this threshold level is only 7% higher than the actual value of the average monthly income of Ukrainian households during the analysis. At the same time, under this condition, the state in Ukraine will be able to transfer part of the financial burden of compensating healthcare costs to insurance companies. According to the calculations made with the help of the game theory toolkit, with the maximization of insurance payments to the population under health insurance contracts, the burden on public health financing in Ukraine could be reduced by 67.7%. The paper was conducted on the data of the ten most potent insurance companies of Ukraine as of 2021 (it is they who accumulate the lion’s share of household insurance premiums), that is, before the start of a full-scale war between the Russian Federation and Ukraine. The obtained results can be used both by insurance companies during the management of insurance premiums and payments and at the level of state management of costs in the field of public health. AcknowledgmentThis study was undertaken as a part of the research projects granted by the Ministry of Education and Science of Ukraine: “Socio-economic recovery after COVID-19: modeling the implications for macroeconomic stability, national security and local community resilience” (registration number 0122U000778); “The impact of COVID-19 on the transformation of the system of medical and social security of population: economic, financial-budgetary, institutional-political determinants” (0122U000781).

The purpose of this work is to identify the functional links between key indicators of scientific activity and socio-economic development and to check whether the quality of scientific activity and the dynamics of innovative development are the key determinants of socio-economic progress. Following the chosen methodology, the paper forms an array of input data that characterizes the level of scientific and innovative activity, economic and social development. The principal component method is used to identify the most relevant indicators from each group and to introduce three latent variables that denote each group separately. A system of simultaneous structural equations is obtained as a result of establishing functional relationships between manifest and latent variables and building a structural model. In addition, the paper determines two clusters of the studied countries to confirm the obtained results through structural modelling. The study is conducted for 35 European countries based on 33 indicators, which characterize the quality of scientific activity, economic and social development during 2014-2020. The obtained system of structural equations confirms the hypothesis regarding the importance of scientific activity quality in terms of ensuring the socio-economic development of the country.

Relevance of the article topic consists in the fact that it is critical to analyse relations between financial, social-economic and healthcare determinants for the post-pandemic recovery of macroeconomic stability. Consideration of these relations can provide complex recovery strategies, which will contribute to economic, medical amd social resiliemce. The latter is extremely important when there are new global challenges to overcome. The manuscript aims at identifying structural and functional connections between social-economic determinants that influence the healthcare sector. To achieve this purpose, we used the following research methods: induction and deduction, analysis and synthesis, comparison and logical generalisation, table and diagram visualisation, observation and assessment, method of principal components, structural modelling. The research data are taken from the World Bank. Totally, the paper analyses statistical data from 15 European countries in 2000-2021. The modelling input data comprise 18 indexes (financial, social-economic, healthcare ones). The constructed structural model provided the following conclusion: if healthcare indexes increase by 1, social-economic development decreases by 0.014. One of the reasons for that is the negative mortality value. The most important aspect is an efficient reaction to epidemics and pandemics. Development of containment plans, including the healthcare scaling and inter-authority coordination is critical for effective crisis management. We should develop a stable economy and policies to provide an affordable and equal access to healthcare for all population groups. Only a complex approach in each separate country may drop mortality and minimise impacts on different aspects of society life. The obtained results can be applied by authorities of public health to improve efficient recovery strategies, raise healthcare standards, make proper decisions for social-economic development.