The green energy transition is associated with the use of a wide range of metals and minerals that are exhaustible. Most of these minerals are limited in access due to small resource fields, their concentration in several locations and a broader scale of industry usage which is not limited exclusively to energy and environmental sectors. This article classifies 17 minerals that are critical in the green energy transition concerning the 10 main technologies. The following classification signs of metal resources were used: (1) the absolute amount of metals used in the current period for energy; (2) projected annual demand in 2050 from energy technologies as a percentage of the current rate; (3) the number of technologies where there is a need for an individual metal; (4) cumulative emissions of CO₂, which are associated with metal production; (5) period of reserves availability; (6) the number of countries that produced more than 1% of global production; (7) countries with the maximum annual metal productivity. The ranking of metals according to these characteristics was carried out using two scenarios, and the index of the availability of each mineral was determined. The lowest availability index values (up to 0.15) were calculated for cobalt, graphite and lithium, which are key battery minerals for energy storage. Low indices (up to 0.20) were also obtained for iron, nickel and chromium. The calculation of the availability index for each mineral was enhanced with linear trend modelling and the fuzzy logic technique. There are two scenarios of demand–supply commodity systems with a pre-developed forecast up to 2050: basic independent parameter probability and balanced fuzzy sum. Both scenarios showed comparable results, but the second one highlighted supply chain importance. Generally, the lowest availability index values (up to 0.15) were calculated for cobalt, graphite and lithium, which are key battery minerals for energy storage. Low indices (up to 0.20) were also obtained for iron, nickel and chromium. The fuzzy logic model helped to reveal two scenarios up to 2050. The two scenarios presented in the current research expose a high level of uncertainty of the projected 2050 forecast.

The start of the full-scale Russian-Ukrainian war caused the largest wave of migration in the 21st century. More than five million Ukrainian citizens left for EU countries within a few months of the start of the conflict. The purpose of this paper is to forecast the level of health care expenditure in Ukraine for 2023–2024, considering the scale of migration and the fall in the level of GDP. The authors propose three scenarios for the development of Ukraine’s economy in 2023–2024, taking into account changes in the age structure of the population, migration, and the amount of health care expenditure: (1) Pessimistic, in which economic growth will resume only in 2024, with a GDP rise of 5.6%, provided that the war concludes at the end of 2022. Under this scenario, inflation will be about 21% in 2023–2024, a slight decrease compared with the previous year. Some 12% of the population of Ukraine will have emigrated, resulting in a corresponding 12% drop in health care expenditure in 2023–2024. (2) Basic (realistic), in which economic growth will be about 5% in 2023–2024, inflation will be under 10%, and migration will have accounted for 5% of the country’s population. Under this scenario, there will be an increase in health care expenditure of more than 40% in 2023–2024. (3) Optimistic, according to which rapid economic growth is expected in 2023–2024, inflation will not exceed 7%, the majority of those who left Ukraine in the early months of the war will return, and health care expenditure will increase by more than 70% in 2023–2024. The methodology of forecasting public expenditure on health care has been based on a six-step cohort method. The results have indicated that the cost of updating the age structure of Ukraine’s population every year will decrease due to the aging of the population, and the overall impact of demographic processes will be negative. The impact of mass migration due to the war creates a significant change in health care costs, requiring administrative bodies to monitor the situation promptly and make appropriate changes to the structure of budget expenditure.

This paper examines the impact of migration and changes in the population’s age structure on government expenditures on education in Ukraine. The cohort method, considering a group of people attending four main types of education, namely, preschool, general secondary, vocational and technical, and higher education, is used to analyze and forecast government expenditures in the medium and long term. The study results show that migration significantly impacts government budget expenditures on education, with the most significant impact being seen on preschool education, followed by higher education, while the impact on general secondary and vocational education is minimal. The number of people receiving education in one of the four main types of institutions funded by state and local budgets was predicted to gradually decrease during the forecast period, except for secondary and vocational education. The overall volume of state expenditures would gradually decrease, both in absolute terms and as a percentage of GDP. Improving educational processes and the quality of budget spending should become an area for state regulation to ensure quality education in all funding conditions. This research’s leading theoretical and practical results show the efficiency of this methodology for analyzing and forecasting budget expenditures on education. They can be helpful in the short and medium terms, considering all potential changes in demographic indicators regarding the population.

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The predictability of asset prices on the exchanges is the most relevant topic of financial research. High/low prices help to analyze the volatility of the commodity price in the current auctions at the stock exchange. In the study we investigate the impact of daily high/low spreads on close/open spreads for non-ferrous and precious metals using the multifractal analysis and the Fractionally Cointegrated Vector Autoregressive (FCVAR) models. For fractional data testing, it is proposed to use an indicator based on the width of multifractal spectrum. Results of full series and positive spreads models for all metals except zinc and lead indicated that any increase in high/low spreads leads to a decrease in close/open spreads. For all negative spreads models this relationship has positive trend.