Aleksandra Kuzior

This paper is devoted to providing affordable housing as a significant factor in public health management, inclusive growth, and SDG. The purpose is to empirically prove changes in the system of country’s population health care depending on the level of providing affordable housing. The empirical base includes time series and panel data for 27 EU countries during 2011–2019. Due to correlation analysis (Shapiro-Wilk testing, Spearman or Pearson correlation, lags in time), regression analysis, and building a dynamic panel estimation model with Sargan testing in STATA, the study empirically confirmed and formalized the impact of affordable housing funding on changes in the system of country’s population health care. In particular, the study found the dependence between overcrowding level and the share of homeowners with mortgages (a decrease of overcrowding level by an average of 0.61% with a time lag of 2 years due to an increase by 1%); the share of tenants on concessional terms/free (0.41% with a time lag of 3 years); and the share of public spending on housing development (0.25% with a time lag of 3 years). The direct relationship between the overcrowding and mortality from dangerous diseases (tuberculosis, AIDS, viral hepatitis, mental and behavioral disorders, diabetes, pneumonia) was also revealed. Public spending on housing under social protection programs (subsidies, etc.) proved to be the least effective. Preference should be given to the development of affordable mortgage lending (faster and greater effect). Generally, it impacts public management decisions in the health care system, social, and housing spheres.

Acknowledgments
The study was funded by the Ministry of Education and Science of Ukraine and performed the results of the project “Economic and mathematical modeling and forecasting, development of methodological and methodological foundations for creating a roadmap for reforming the health care system in Ukraine, taking into account behavioral, social, economic and legal determinants” (Agreement БФ / 24-2021).
This study received funding under the research subsidy of the Department of Applied Social Sciences of the Faculty of Organization and Management of the Silesian University of Technology for the year 2022 (13/020/BK_22/0072).

Trust in public services is a driving force of socio-economic development. It ensures effective dialogue between citizens and government and becomes especially relevant in the digitalization. The lack of trust in the public sector can make reforms ineffective and the achievement of the goals of the Ministry of Digital Transformation within the framework of the State Strategy for Regional Development of Ukraine until 2027 in question. For this reason, marketing tools in public services should be directed toward achieving greater trust in those services and the government. This paper analyzes the current research on public sector marketing and its role in building trust as a foundation for the country’s digital development. Implementing multi-level bibliometric (Scopus tools), network (VOSviewer), and comparative (Google Trends) analysis, this study traces the development of marketing research on the process of public services delivery transformation from traditional to digital. 922 studies published between 1968 and 2022 were analyzed. The results show that public sector marketing is a multidisciplinary field of scientific research that is actively developing. Cluster analysis demonstrated that its modernization and the relationship between marketing in services and consumer behavior are the smallest and newest spheres of research. Researchers consider relationship marketing the principal mechanism for building trust in the public sector. This study found little research on the impact of marketing tools on consumers’ trust in public services. The comparative analysis confirmed the hypothesis of the need to change marketing tools due to the massive shift from offline to online services.

Acknowledgment
The authors acknowledge the funding in terms of the projects “Simulation modeling of the trajectory of the impact of behavioral attractors on macroeconomic stability: the role of transparency and public trust (0121U100469)” and “Modeling mechanisms for de-shadowing and de-corrupting the economy to ensure national security: the impact of the financial behavioral patterns transformation (№ 0122U000783)”.

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.

Acknowledgment
This 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).

Changes in the logistics of energy resources and their potential shortage are causing a review of the EU energy policy. The energy sector significantly affects the progress toward achieving climate policy goals due to significt greenhouse gas emissions. The REPowerEU plan, implemented in the EU27 to overcome the energy crisis, requires new forecasts of greenhouse gas emissions due to a change in European energy policy.
This paper aims to examine the consequences of the management of the energy crisis caused by Russia’s invasion of Ukraine on EU climate policy. This study focuses on forecasting greenhouse gas emissions in the EU until 2030 and uses the Seasonal ARIMA model based on quarterly time series in the EU27.
Depending on energy management and changes in energy policy to overcome the energy crisis, a positive or negative scenario for greenhouse gas emissions may occur. An important parameter that should be considered when determining the scenario of the EU energy development according to climate policy was defined by correlation analysis.
According to the negative scenario and under the influence of the effects of the Russian invasion of Ukraine, the value of greenhouse gas emissions in the EU at the beginning of 2030 will be 0.752911 tons per capita. The positive scenario shows greenhouse gas emissions can be reduced to 0.235225 tons per capita.
The study results proved two extreme scenarios of greenhouse gas emissions, depending on how to overcome the energy crisis.

Acknowledgment
The authors appreciate the copyright holder: © European Union, 1995–2022, as well as the source of the extracted data, which is the European Commission website, Eurostat http://ec.europa.eu/eurostat (accessed on 16 October 2022).
This study was funded by the European Union (the project No. 101048079 – EU4SmartED – ERASMUS-JMO-2021-HEI-TCH-RSCH); by the Ministry of Education and Science of Ukraine (projects No. 0122U000788, 0122U000769, 0121U109553, 0120U102001, 0122U000777).
This research was funded by Faculty of Organization and Management of the Silesian University of Technology (grant number: 13/990/BK_23/0178).

Transparency is a fundamental necessity for the insurance market in the modern fast-changing and digital world. The study aims to establish, based on bibliometric analysis, the research trends and subject areas of the insurance market transparency, including the impact of digital technologies, regulatory initiatives, and the internal policies of insurance companies. A bibliometric analysis of papers published in the journals indexed by the Scopus database for the years 1988–2023 was conducted to achieve this goal. Five clusters have been identified based on the analysis of the shared use of keywords, demonstrating the multidisciplinary nature of the research subject. They cover government regulation and risk management; ethics; technological innovations in increasing transparency; transparency of prices and costs in health insurance; and state medical insurance transparency. The analysis of insurance market transparency trends has allowed identifying four key stages of development: post-crisis regulatory mechanisms (2013–2016), Solvency II regulation effectiveness (2017–2019), transparency during the pandemic (2020–2021), and the impact of digital innovations since 2021. Spatial clustering made it possible to identify five groups of countries whose representatives are co-authors of research on insurance market transparency. The leading countries in research on insurance market transparency are the USA, the UK, and Germany.

Acknowledgment
This research was funded by the Ministry of Education and Science of Ukraine (projects No. 0122U000774 “Digitalization and transparency of public, corporate and personal finance: the impact on innovation development and national security”, No. 0123U101945 “National security of Ukraine through the prevention of financial fraud and money laundering: war and post-war challenges”). This research was funded under the research subsidy of the Faculty of Organization and Management of the Silesian University of Technology in Poland for the year 2023 (13/990/BK_23/0178).

This study aims to examine whether the labor productivity of the US population directly depends on public or private insurance coverage of people, employment level, life expectancies, spending on the public health system as a percentage of GDP, and spending on the public health system in natural terms.
Empirical testing was carried out on the USА statistical data for 1987–2021 using a regression model with the fitting procedure backward stepwise selection (in Statgraphics software), and a multivariate adaptive regression spline MARS (using Salford Predictive Modeler software). The research hypothesis was confirmed for only two indicators: life expectancies and spending on the public health system in natural terms. Their impact on labor productivity appeared to be directly proportional. As an indicator, spending on the public health system has a greater impact on the change in productivity (0.0058%), whereas life expectancy has a lesser effect (0.0047%). The study showed that the MARS model provides more objective and accurate results compared to the regression model with the fitting procedure – backward stepwise selection. This conclusion is based on a comparison of real data modeled by both methods. The study proved that labor productivity in the USA grew yearly from 1987 to 2021 (the constant term in the MARS model’s regression equation is +0.48428). To calculate the specific values of labor productivity for each year, a model was developed depending on the optimal basic functions (automatically generated by the MARS model depending on the current values of life expectancies and spending on the public health system in natural terms).

Acknowledgment
This study is funded by Department of Applied Social Sciences of the Faculty of Organization and Management of the Silesian University of Technology for the year 2023 (grant number 13/020/BK_23/0081).

As cities increasingly adopt smart technologies and seek to foster innovation-driven economies, it is vital to understand how smart city development relates to the strength of local startup ecosystems. This study investigates whether a statistically significant relationship exists between a city’s performance in the smart city ranking and the strength of its startup ecosystem. The study employed available data from the Global Startup Ecosystem Report (by Startup Genome) and the Smart City Index (SCI by the IMD World Competitiveness Center). A balanced panel regression analysis was conducted on a dataset comprising 77 cities across the years 2020, 2021, and 2023 (2022 is excluded as the SCI was not published). The findings reveal that the Random Effects model yielded statistically significant results, indicating a weak (R² = 25.63%) but significant inverse relationship between SCI and startup ecosystem development, which means cities that rank higher on smart city metrics tend to show lower levels of startup ecosystem performance. This counterintuitive result challenges the assumption that technologically advanced cities automatically provide fertile ground for entrepreneurial activity. One possible explanation is that smart cities, dominated by large tech players and rigid governance structures, may present entry barriers for emerging startups. High operational costs, regulatory constraints, and a focus on large-scale infrastructure projects may disincentivize startups from localizing their innovations within these environments. Although the R² suggests that other variables beyond the smart city ranking influence startup development. This study highlights the need for urban policies that actively integrate startup-supportive mechanisms into smart city strategies.