Viktoriia Marhasova

During the full-scale war, educational institutions of Ukraine were at the epicenter of hostilities and suffered from attacks and bombing by the russian army: many schools, colleges, and universities were destroyed or damaged, and tens of thousands of students and teachers became refugees or forced displaced persons, lost their property and housing. Chernihiv Polytechnic National University (CPNU), located in the northern border region of Ukraine, was also significantly affected.
This study aims to systematize the features of the functioning of CPNU under siege, bombing, and shelling. An analytical method was used to assess statistical reports available in open access on the official websites of CPNU and the Ministry of Education and Science of Ukraine. The paper identified that during active hostilities, 25% of the university’s infrastructure was destroyed by shelling and bombing; 37 teachers and researchers emigrated; about 4% of students stopped their studies. Currently, CPNU suffers from loss of human capital, physical destruction of educational and research infrastructures, lack of R&D financing, increased cyber security threats, and violation of the educational process. In response to military challenges, the university management rethinks and forms a new vision for further development based on sustainability principles to recover educational and research infrastructures, promote human capital development, and modernize the learning environment. The new vision of CPNU integrates “7R” principle – seven key pillars covering different directions of its operation (teaching, research, innovation, international activity, etc.).

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.