The study examines the relationship between internal determinants, external determinants and the profitability of state-owned commercial banks. We use pooled regression, fixed effect, and random effect models on the case of the top five Chinese state-owned commercial banks from 2007 to 2019. The results show that internal factors, measured by size, credit quality, and liquidity, significantly positively influence banks’ profitability. State-owned banks that have larger sizes, higher credit quality, and higher liquidity have accordingly higher profitability than other banks. On the contrary, the external factor, measured by the natural logarithm of GDP, negatively influences banks’ profitability. The decrease in GDP leads to higher profitability of state-owned commercial banks in China. Our results provide insight into the profitability of state-owned commercial banks, considering the latest changes in the Chinese banking industry.

This article investigates the connection between US logistics companies’ commitment to environmental, social and fair governance (ESG) strategy and their performance on the US stock market during the 2007–2022 period. The research considers historical data analysis, CAPM and a comparison of optimised portfolios. According to the results of the analyses, ‘green’ logistics stocks are less volatile, and hence less risky, and more profitable compared to ‘non-green’ logistics stocks. The Great Recession (2007–2009) and the COVID-19 pandemic (2020) had the greatest impact on stock volatility, in terms of the US stock market. Optimised during the time of the Ukrainian crisis, green logistics portfolios were shown to have higher returns, but also risks and Sharpe ratios, than ‘non-green’ ones. The results confirm there to be a connection between companies’ commitment to ESG strategy and enhanced stock performance, which contributes to the importance of the ESG agenda.

The development of innovation at a regional level in a transition economy is characterised by complex multidirectional processes of generating and commercialising innovation, indicating the need for systematic research and rethinking of the existing methods of managing such territorial entities to stimulate innovation. For the successful introduction and implementation of innovative solutions, the deployment of appropriate amounts of intellectual, material and financial resources as well as their concentration in space and time is important. This article aims to develop a model for assessing the effectiveness of regional innovation systems (RISs) during the shift from the transition economy to the market economy. The authors developed a two-stage data envelopment analysis (DEA) model connected with patent activities and the output of innovative goods and services. The model’s application made it possible to build maps describing the rating of regions concerning the performance indicator and to identify the availability of unutilised resources. For example, we identified efficient and inefficient regions in terms of producing innovative products, which is especially important for developing additional measures for developing the institutional environment of regions with considerable resources but very low utilisation efficiency. The data obtained will allow for more effective management of the structural elements of RISs as well as the detection of changes in the dynamics of key development indicators by identifying the size of efficiency reserves and the causes of their occurrence at the individual subject level.

The manufacturing industry makes a significant contribution to Russia’s GDP and exports, but it faces problems that hinder its development. The aim of this study is to estimate the relationship between intellectual capital and performance indicators of Russian manufacturing companies. The study analysed a sample of 23,494 observations of Russian manufacturing companies for the 2017–2020 period. The value-added intellectual coefficient (VAIC) and its components were used to evaluate the impact of intellectual capital on firm performance using polled ordinary least squares, fixed, and random effects models. Intellectual capital significantly and positively affects the performance of companies in both structural and human terms—both through the integrated coefficient VAIC and in the context of individual components of intellectual capital. However, the impact of structural and human capital on performance indicators is significantly lower than the impact of capital employed. There is a distinct focus of enterprises on making profit through the use of company assets, while in the case of Russian manufacturing companies, the potential for profit generation from structural and human capital remains unfulfilled.

This article investigates factors that explain road crash severity levels in Saint Petersburg, Russia, during the 2015–2021 period. The research takes into account factors such as lighting conditions, weather conditions, infrastructure factors, human factors, accident types, and vehicle category and color to assess their influence on crash severity. The most influential accident type is run-off-road crashes, which are associated with an 11.2% increase in fatal accidents. The biggest reason for the increase in fatal accidents due to road infrastructure conditions is road barrier shortcomings (2.8%). Road infrastructure conditions, such as a lack of road lighting, have a significant effect on fatal outcomes, increasing them by 12.6%, and this is the most influential factor in the analysis. The obtained results may serve as a basis for Saint Petersburg authorities to develop new road safety policies.