Getting access to sufficient funding is the keystone for the development of any business, but especially for small and medium enterprises (SMEs). These economic entities are crucial players in the global economy since they include almost 90% of companies, provide jobs for nearly 50% of the global workforce, and enhance long-term economic growth. In this context, our study explores important sources concerning the financing of small and medium enterprises and their impact on economic growth during the period 2005–2020 with data from SMEs covering the 28 countries belonging to the European Union. The set of predictors included Strength of legal rights index, Days sales outstanding, Bad debt loss, Interest rate, Bank support, Business angels, Private lenders, and Public support. The set of dependent variables included Cost of loans, Equity fund, GDP growth rate, and Value added growth rate. Our methodological approach was complex, it considered a panel data analysis with a first-difference generalized method of moments estimator and a multiplex time series analysis. The novelty of the study resides in combining the two methods in order to investigate significant drivers of economic growth across the EU. Empirical results showed that economic growth was mainly triggered by predictors such as Interest rate, Business angels, Bank support, and Public support. Moreover, the valuable mathematical insights elicited by the multiplex time series analysis suggested that European economies cooperated intensively through SME activities. Based on our empirical results, national and regional authorities should enact adequate policies to support business endeavors of small and medium enterprises.

This research study examines the factors that determined economic growth measured by gross domestic product, gross value added, final consumption expenditure of households, and gross fixed capital formation across a sample of 36 European countries during the time frame Q3 2018−Q3 2021. We conducted a panel data analysis with the first-difference generalized method of moments (GMM) approach and cross-section fixed effects. Empirical results estimated with four econometric models indicated a significant robust impact of the independent variables exports, imports, foreign direct investment inflow, foreign direct investment outflow, social contributions, and wages on economic growth proxies. In addition, multiplex network analysis was used to investigate the connection architectures of the 36 countries, and it yielded statistical measurements for all layers in the multi-layered structure. Relevant policy implications of reported empirical results are also addressed.

The study investigated the impact of factors such as non-performing loans, CO₂ emissions, bank credit, and inflation on the variable sustainable economic growth for India, Brazil, and Romania during the period 2005–2017, through a panel data analysis. Specifically, we investigated the timeline before, during, and after economic turmoil, with a special focus on the global financial crisis. Our empirical results are valuable for both developing and developed nations. As a first result, we showed that CO₂ emissions increased the level of economic growth, but in this context, authorities should design suitable policies to limit its impact on the overall society. In addition, a single supervision mechanism increased the level of sustainable economic growth. Last but not the least, the period during and after the global financial crisis, sustainable economic growth decreased under the influence of bank credit, inflation, and non-performing loans. Within this framework, public authorities are called to design efficient economic, fiscal, and monetary policies.

The study focuses on the effects of imports, exports, financial direct investment inflow and financial direct investment outflow on sustainable economic growth expressed by various macroeconomic indicators (gross domestic product, gross domestic savings, gross domestic capital) using the least squares panel method. Sample data were selected for ten Central and Eastern European (CEE) countries and the time frame considered was 2005–2016. Generally, transitional economies have to incorporate strong savings and a steady capital formation in order to achieve higher economic growth via foreign direct investment. Results showed that the analyzed factors played a major role in the sustainable economic growth of CEE countries. Another important and valuable insight of this study is that the financial sector steers the process of achieving sustainable economic growth across CEE countries.

Global economic growth is based on increased consumption of electricity both from renewable resources, such as water and wind and non-renewable resources, such as coal (lignite), natural gas or petroleum. Coal continues to represent an important energy source in the European Union, particularly in Germany, France and Spain, where it accounts for 15% of the primary energy, out of which 80% is used in electricity supply. The coal (lignite) mines from the Oltenia region contribute significantly to generating power in Romania. The study aims to show that an increase in production within the coal (lignite) mining industry can be determined by increasing direct and indirect costs or by increasing variable costs and profit. We also examine the non-linear relation between variable costs and production on the one hand and between profit and production on the other hand. Our results show that there is a concave relationship between variable costs and production, and also a concave relationship between profit and production, which indicate that Romanian coal enterprises have an optimal production level that maximises both variable costs and their profitability. In addition, a robustness check of our results confirms that variable costs and profitability decrease as they move away from their optimal level.

Forecasting price changes is very important for the process of estimating and managing market risk in financial markets. Price changes in financial markets may also depend on non-market factors. Considering this situation, the study investigates the effect of the COVID-19 pandemic on Borsa Istanbul. It tackles changes in the fractal dimensions of the time series obtained with the daily closing prices of stocks traded on Borsa Istanbul (BIST). According to the results of the sector-based analysis, we found that fractal dimension changes were quite effective in price estimation.