Nataliia Versal

The move to sustainable development and building a carbon-low economy needs funding. In this regard, a new direction in finance – green (sustainable) finance – has emerged. One of the green finance instruments is green bonds, first issued by supranational financial institutions. This paper aims to identify the features of green bond issues and implemented green projects by the World Bank (the WB) and the European Bank for Reconstruction and Development (the EBRD). Data were obtained from databases and reports of the WB, the EBRD, and the Climate Bonds Initiative. Data analysis was provided using statistical methods, particularly descriptive and comparative statistics. A positive trend in the issue of green bonds in the volumes and timing of the WB and the EBRD was revealed, despite the shift in emphasis caused by COVID-19. Renewable energy, energy efficiency, and clean transportation remain the primary directions of the WB, and the EBRD green projects amounted to more than 60% of total projects funding. The geography of green projects financed through the WB and the EBRD green bonds indicates that green projects are receiving significant funding from countries facing environmental challenges and demonstrating intent to green transition (the WB – China and India, the EBRD – Turkey, Poland, and Egypt). Supranational financial institutions were the first to come to the forefront of sustainable development funding and are now spearheading the creation of new financial instruments aimed at financing both green and social projects, leading to the emergence of sustainability bonds.

Acknowledgment(s)
The authors would like to thank the participants of the 1st International Conference on Sustainable Development (SDL 2021) for providing the valuable remarks and a fruitful discussion. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Accurate forecasting of consumer loan market behavior gives banks a huge potential to optimize their credit strategies by proactively adapting to external changes. This study aims to analyze and predict consumer loan demand, supply, and profitability in the Ukrainian banking sector. Using a systemic dynamic approach, the interplay of five key factors is considered: central bank policies, GDP fluctuations, changing competitive landscape driven by FinTech companies, investment in government bonds as an alternative to loan granting, and severity of credit risk management.
The developed dynamic model for the bank consumer loan market in Ukraine offers predictive capabilities enhancing decision-making and strategic planning in the banking sector and can be adapted in open small economies. Within the proposed systemic dynamic model, five scenarios were explored. Compared to the base scenario, a 4 p.p. increase in the key policy rate results in UAH 4.7 billion decrease in demand for bank consumer loans and a UAH 0.55 billion reduction in lending profitability based on the year’s results. Fall in GDP by 6 p.p. leads to a decrease in the supply of bank consumer loans by UAH 6.9 billion and a decrease in lending income by UAH 1.3 billion based on the year’s results. Scenario with the decline of FinTech portfolio by 20 p.p. quarterly leads to an increase in demand for bank consumer loans of UAH 8 billion. A 4 p.p. rise in government bond yields leads to a UAH 17 billion reduction in the supply of consumer loans in the same quarter.

The increasing integration of Artificial Intelligence (AI) into education requires studying the motives for its use among students. This study aims to identify the key motivations for economics students to use AI and compare these motivations by grade level and gender. The study examines satisfaction with the use of AI and analyzes the number of AI tools used.

An anonymous empirical study was conducted among 264 students from the Faculty of Economics at Taras Shevchenko National University of Kyiv, Ukraine. Data analysis included descriptive statistical methods, non-parametric statistical methods, and exploratory factor analysis.

The study found that students’ main motivations for using AI are the automation of routine tasks (34.2%) and the need to save time (21.5%), while 18.7% use AI to compensate for lack of experience. Among Bachelor’s students, motivations such as automating routine tasks and saving time increased from 53% to 58% over the course of their studies, while lack of experience decreased from 22% to 15%. In contrast, Master’s students showed a decrease in routine automation (from 36% to 28%) but an increase in the need to compensate for lack of experience (from 15% to 28%) and to save time (from 18% to 25%). In terms of gender, men are more likely to use AI for learning and personal development, while women are slightly more likely to use AI for work. More than 38% of respondents say they need to use at least 2 AIs to achieve their goals.

Acknowledgment
This publication is based upon work from 24-PKVV-UM-002, ‘Strengthening the Resilience of Universities: Czech-Ukrainian Partnership for Digital Education, Research Cooperation, and Diversity Management,’ supported by the Czech Development Agency and the Ministry of Foreign Affairs under the initiative ‘Capacity Building of Public Universities in Ukraine 2024.’