Oleksandr Kubatko

This paper examines the economic relationships between oil price volatility and socially-economic development of 14 Organization of the Petroleum Exporting Countries (OPEC) using the annual panel data for the period 1990–2014 obtained from the World Bank (WB) statistical data sets. Hausman specification test has been performed to choose the method of panel data analysis, and the results were in favor of fixed effects estimation. The main findings indicate the direct relationship between economic growth and oil price volatility. The research supports the hypothesis that an increase in crude oil prices is positively related to GDP, and a 10% increase in oil prices correlates with 0.6-4% GDP improvements. Structural changes in employment in favor of service sector are negatively correlated with GDP per capita. Changes in GDP structure in favor of oil rents on 10% lead to the shrinking of GDP on 1%. Life expectancy at birth, as an indirect indicator of health, positively influences the economic growth indicators and an improvement in life expectancy on one percentage leads on average to 1% growth in GDP and 0.5-1.33% growth in GDP per capita. Energy efficiency improvements are positive drivers of GDP values at OPEC, and our findings suggest that a 10% increase at GDP per unit of energy use leads to 3% increase of GDP itself. The study recommends investing in energy efficiency, human capital, and capital formation to guarantee long-run economic development and prosperity of OPEC counties.

The development of human civilization is related to the constant change of economic formations, and the current social and economic situation is determined by such concepts as Society 5.0, Fourth, and Fifth Industrial Revolutions (FIR, FiIR). The paper aims to estimate the change of human role in each economic formation caused by industrial revolutions. A structured review methodology with a focus on biological, labor, and personal entity of human within the industrial revolutions is used. The description of the changes between the biological, labor, and personality entities of human in various socio-economic formations is discussed. The human as a biological entity is not changed in the first four industrial revolutions, while the FiIR tries to change the biological entity through augmenting the physical capacity. The human as a labor entity is not changed in the first three industrial formations, while the FIR tries to replace the majority of physical human jobs and opens the gate for creative economy and decisions-making. The direct labor participation is minimized within FIR since the economic systems move to the transition to the dominant role of cyber-physical systems. The personal human development is triggered within the FiIR, since informational diversity in economic systems is actualized, and conditions for creative jobs within the creative economy are formed. The biological, labor, and personality entities of human are sequentially actualized within the economic formation caused by industrial revolutions.

Over the years, technological progress has accelerated highly, and the speed, flexibility, human error reduction, and the ability to manage the process in real time have become more critical and required production companies to adapt production and business models according to the needs. The demand for real-time decision support systems adapted to these raising business needs is continuously growing. Nevertheless, businesses usually face challenges in identifying new indicators, data sources, and appropriate financial modeling methods to analyze them. This paper aims to define and summarize the main financial/economic forecasting methods for production companies in the context of Industry 4.0. Main findings show forecasting accuracy of up to 96% when combining economic and demand information, optimal forecasting period from 10 months to five years, more frequent use of soft indicators in forecasting, the relationship between company’s size and production planning. Four groups of indicators used in financial modeling, such as (I) production-related, (II) customers’ and demand-oriented, (III) industry-specific, and (IV) media information indicators, were separated. The analysis forms a suggestion for decision-makers to pay more attention to the forecasting object identification, indicators’ selection peculiarities, data collection possibilities, and the choice of appropriate methods of financial modeling.

Acknowledgment
This work was partly supported by Project No. 0121U100470 “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy”.

Rapid economic reforms and proper GDP growth in China has affected the regional development of Chinese provinces. This study aims to estimate the degree of economic and environmental disparities within Chinese provinces for developing policy recommendations of regional transformation. The reduced log-linear specification of endogenous growth model is used for the estimation of convergence rates within Chinese provinces. The empirical results prove that an increase of 1% in GDP per capita basic year reduces the economic growth rate by 0.1% in the reference year. Thus, the ratio of the average per capita income in the wealthiest group to poorest provinces accounted for the factor 9.6 in 1995 and factor 4.1 in the year 2015, which means a reduction of disproportionate development. Environmental convergence trends were also found and less polluted provinces eventually increase emissions at higher rates than the initially polluted ones. With the pass of time, all provinces do move to the same steady state in environmental parameters. The speed of the economic and environmental convergence in China provinces is rather slow, and the economic growth was achieved by great sacrifices of an environment, since all provinces are striving to the same steady state in terms of pollution increase. The industrialized regions due to the presence of significant financial resources should pay more attention to the protection of the environment using all the available economic potential. At the same time, both initially poor provinces and rich have to develop more profoundly agriculture, tourism, recreation, and other environmentally friendly industries to improve economic performance.

The study’s relevance relates to the transformation of the human capital reproduction during the transition to a new socio-economic model and changes (digitalization, cyberization, customization, etc.) that are now taking place within Industries 4.0 and 5.0. The purpose of the study is to formulate the content and key directions of learning processes based on modeling and the formation of digital twins for the production and consumption of goods. The research method is based on the analysis of structural links in socio-economic systems, where the potential of human capital is realized. The study describes a trialectic model for the system development mechanism, which gives grounds to distinguish three types of essential components of implementing the specialists’ competencies (material, information, and communication). Based on the concept of “system of systems”, the necessity of multifunctional training of specialists for socio-economic systems is substantiated and shown on the list of personal knowledge/skills in the renewable energy sector. Recent trends in the reproduction of human capital, such as intellectualization, increased communication, internationalization, acquisition of skills, customization, and communication with consumers, are stated in line with Industries 4.0 and 5.0. The potential for future research is aimed at harmonizing relations between humans and cyber-physical systems, motivating the needs for self-development, and using disruptive technologies in the reproduction of human capital.

Acknowledgment
The publication contains the results of research of the European Commission grants “Jean Monnet Chair in EU Economic Policies and Civil Society” (619878-EPP-1-2020-1-UA-EPPJMO-CHAIR) and EU legislative, economic and social transition to sustainable society within Industry 4.0 and 5.0 (619997-EPP-1-2020-1-UA-EPPJMO-CHAIR).
The paper is prepared within the scientific research projects “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy” (No. 0121U100470) and “Fundamentals of the phase transition to the additive economy: from disruptive technologies to institutional socialization of decisions” No. 0121U109557), funded by the general fund of the state budget of Ukraine.

This paper investigates the effect of the Brexit vote on the connection between UK stock market expectations and US stock market returns. To gauge UK stock market expectations, the option-implied volatilities of the FTSE 100 index are calculated in the period starting five months before and ending four months after the Brexit referendum. To keep the analysis “clean”, it stops right before the 2016 US presidential elections. It uses an OLS regression to estimate the change in the relationship between US and UK stock market expectations.
The main findings show that the US and UK stock markets became somewhat less integrated four months after the Brexit referendum compared to the five months before it. The S&P 500 Index returns have a statistically significant impact on implied volatilities of the FTSE 100 only before the Brexit referendum. However, the British risk-free rate (LIBOR) became a statistically significant factor affecting FTSE 100 implied volatilities only after Brexit. This analysis may be used by decision-makers in the money management industry to act appropriately during Black Swan events. When UK citizens unexpectedly voted in favor of Brexit, the risk-free rate dropped, making it cheaper to invest, increasing the Sharpe ratios of equity portfolios. Coupled with increased uncertainty, this caused portfolio reallocations. In turn, expected volatility measured by options-implied volatility increased.

Acknowledgment
The authors would like to thank Olesia Verchenko for critique, a KSE M.A., external defense reviewer for helpful comments.

Digitalization, dematerialization of production and consumption, and structural shifts in the direction of service economy forming do promote to reduction of material use and sustainable development. The paper aims to investigate the role of digital, structural, economic, and social factors in sustainable development promotion in OECD countries. The paper uses the data on digital achievements, social and economic development of OECD member states from World Bank data sources for the period 2007–2018. The random-effects GLS regression model is used, and empirical regression models to estimate the influence of key factors related to digital transformation on GDP per capita and CO2 emissions per capita are constructed. The results of the regression analysis show that using the number of Internet users as an indicator for achievement in digitalization has a positive and statistically significant influence on GDP per capita due to lower transaction costs and higher share service economy. An increase in urbanization rates (as an indicator of capital concentrations and labor specialization) by one percent promotes a GDP per capita increase of 299 USD. Also, an increase in Gini coefficient by one percentage point correlates with decrease in GDP per capita on 196 USD and the reduction of CO2 per capita by 0.12 tones due to the structural shifts in aggregate demand. Still, improvements in digital transformations have no significant environmental effect in OECD members, while processes related to urbanization, income inequality, and share of industrial output are important drivers for CO2 per capita reduction.

Acknowledgments
The paper contains the results of a study conducted within the framework of research projects: “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy” (No. 0121U100470); “Fundamental bases of the phase transition to an additive economy: from disruptive technologies to institutional sociologization of decisions” (No. 0121U109557).